adam supplement to the taug -diabetes - cdisc...adam supplement to the taug -diabetes . version 1.0...

ADaM Supplement to the TAUG-Diabetes Version 1.0 (Provisional)

Prepared by the CFAST Diabetes ADaM Sub-Team

Notes to Readers • This is Version 1.0 of the CDISC ADaM Supplement to the TAUG-Diabetes. It makes use of domains and

assumptions which are not final as of its publication, and is therefore a provisional, rather than final, release. • This supplement is intended to be incorporated into the next version of the TAUG-Diabetes as Section 5,

Analysis Data. • This document is based on ADaM v2.1 and ADaMIG v1.0.

Revision History

Date Version Summary of Changes 2015-12-18 1.0 Provisional Release 2015-07-17 1.0 Draft Draft for Public Review

See Appendix C for Representations and Warranties, Limitations of Liability, and Disclaimers.

CDISC ADaM Supplement to the TAUG-Diabetes (Version 1.0 Provisional)

© 2015 Clinical Data Interchange Standards Consortium, Inc. All rights reserved Page 1 Provisional Dec 18, 2015

CONTENTS

1 INTRODUCTION ................................................................................................................. 2

2 SUBJECT-LEVEL ANALYSIS DATA: ADSL .................................................................. 3 2.1 STRATIFICATION VARIABLES ................................................................................................................................ 3 2.2 ADSL EXAMPLE ................................................................................................................................................... 5

3 ANALYSIS OF HYPOGLYCEMIC EPISODES ............................................................... 8 3.1 HYPOGLYCEMIC EPISODES ANALYSIS DATASET ................................................................................................... 8 3.2 HYPOGLYCEMIC EPISODES ANALYSIS RESULTS .................................................................................................. 11 3.3 HYPOGLYCEMIC EPISODES SUMMARY DATASET ................................................................................................. 12 3.4 HYPOGLYCEMIC EPISODES SUMMARY ANALYSIS RESULTS ................................................................................ 15

4 ANALYSIS OF GLYCATED HEMOGLOBIN ............................................................... 18 4.1 HBA1C ANALYSIS DATASET ............................................................................................................................... 18 4.2 HBA1C ANALYSIS RESULTS ................................................................................................................................ 20

4.2.1 Longitudinal Repeated Measures Model .............................................................................................. 20 4.2.2 Categorical Analysis............................................................................................................................. 22

5 ANALYSIS OF GLUCOSE LEVELS ............................................................................... 23 5.1 SELF-MONITORED GLUCOSE PROFILE ANALYSIS DATASET ................................................................................ 23 5.2 SELF-MONITORED GLUCOSE ANALYSIS RESULTS ............................................................................................... 27

5.2.1 Longitudinal Repeated Measures Model .............................................................................................. 27 5.2.2 Self-Monitored Glucose Plots .............................................................................................................. 29

5.3 MIXED-MEAL TOLERANCE TEST DATASET ......................................................................................................... 30 5.4 MIXED MEAL TOLERANCE TEST ANALYSIS RESULTS ......................................................................................... 34

APPENDICES ............................................................................................................................. 36 APPENDIX A: CFAST DIABETES ADAM SUB-TEAM ................................................................................................... 36 APPENDIX B: REFERENCES ........................................................................................................................................... 36 APPENDIX C: REPRESENTATIONS AND WARRANTIES, LIMITATIONS OF LIABILITY, AND DISCLAIMERS ........................ 37



1 Introduction This ADaM Supplement to the TAUG-Diabetes demonstrates the use of the CDISC Analysis Data Model (ADaM) to create datasets to support the analysis of statistical endpoints common to diabetes trials. Diabetes is a complex disease for which there are many clinical assessments. In turn, these clinical assessments can be used to derive a variety of statistical endpoints used to assess interventions. This document is focused on describing analysis data for three areas of clinical assessments, namely hypoglycemic events, HbA1c, and glucose. This is a supplement to version 1.0 of the CDISC Therapeutic Area Standards User Guide for Diabetes (TAUG-Diabetes v1.0), and in the future these two documents are intended to be combined. It is important to note that the examples in this supplement were chosen in order to illustrate different ADaM concepts and data structures. The examples are not meant to be applicable to every possible diabetes trial since the trial objectives and the primary endpoints will dictate the actual analyses that are performed. The analysis of hypoglycemic events lends itself to creation of statistical endpoints that relate to events, such as incidence or prevalence of events or event severity, and the use of the Occurrence Data Structure (OCCDS) in ADaM. The analysis of the continuous measures of HbA1c and glucose is multifaceted where statistical endpoints may range from continuous measures such as change from baseline or percent change from baseline to categorical measures such as a binary response based on achieving a pre-defined criterion. These data may be analyzed at single pre-defined point in time (e.g., after 8 weeks of treatment) or longitudinally. As such these endpoints can be analyzed through the use of the ADaM Basic Data Structure (BDS). An example of a subject-level analysis dataset (ADSL) is also provided and is based on ADaM. Examples of statistical data summaries, in tabular or graphic form, are also included in this document. These table and figure displays are for illustration purposes. They are not meant to imply any standard analysis presentation format or analysis method and are included to provide examples of ADaM analysis results metadata. In summary, these examples are not meant to make recommendations as to the use of these endpoints, the methods for the endpoints, nor the exact statistical methodology. It is important that each study be evaluated individually and that current ADaM documentation is referenced in order to accurately and robustly design ADaM datasets. This supplement is not intended to illustrate every possible variable that could or should be included in analysis datasets created for statistical analysis of diabetes endpoints, but rather is intended to be descriptive and illustrative of the use of the ADaM model. Therefore, all examples of analysis datasets are abbreviated in nature. The examples should not be interpreted as requirements for the statistical analysis of diabetes data. Additionally, the metadata and derivations presented are for illustrative purposes only and are not meant to imply a universally accepted definition or derivation of the variables. Please refer to Version 2.1 of ADaM and Version 1.0 of the Analysis Data Model Implementation Guide (ADaMIG) for required background about the ADaM and the ADaM data structures.



2 Subject-Level Analysis Data: ADSL The ADSL dataset structure for an individual trial has one record per subject and contains required variables (as specified in the ADaMIG) plus other subject-level variables that are important in describing a subject or the subject’s experience in the trial. Examples of typical ADSL variables include population flags, planned and actual treatment, demographic information, randomization factors, subgrouping variables, baseline values of important measures, and important dates. ADSL variables that describe subject characteristics or disease state are often the means to creating important subpopulations. When creating new variables for these types of data, variable name and naming fragments should be chosen to represent the content of variables, as opposed to meaningless names such as VAR01, VAR02, etc. Before illustrating an ADSL specific to a diabetes trial, this supplement presents a proposal for the management of stratification variables.

2.1 Stratification Variables This supplement presents a proposal for the representation of the description and the associated values for stratification factors used during randomization and treatment assignment. Before describing the proposed variables, the following brief summary of issues related to stratified randomization is provided: Stratified randomization is used to ensure balance of treatment assignments across one or more prognostic factors. A prognostic factor is an aspect of the disease or a characteristic of the subject that may influence treatment response. The prognostic factors used to stratify the randomization are specified in the protocol. As a simple example, suppose age group (<50, >=50) and gender (male, female) are considered important prognostic factors. When a subject is deemed eligible for randomization, their individual values of these factors are determined at the site and used as input to the randomization process to determine their treatment assignment. The situation may occur where the value of a factor that was used for randomization was later discovered to be in error. For example, suppose a subject was randomized according to the age group of <50 and male. Later it was discovered that the subject was actually 54 and therefore should have been randomized according to the age group of >=50 and male. If this situation happens too often, the balance in treatment assignments across these factors is in question which may then result in the use of sensitivity analyses. Therefore, there is an analysis need to have two sets of values to describe the stratification factors. In this document, these two sets of values are referred to the “as randomized” values and the “as verified” values. At present, there is not a standard method for representing the randomization strata factors and values in SDTM-based datasets. Depending on the randomization process, it might be unnecessary to represent variables and values specific to stratification in SDTM-based datasets if the information can be found within an appropriate domain. For example, if age and sex were used as stratification factors, then the DM variables AGE and SEX should appropriately reflect values used for randomization. However, more sophisticated randomizations or more complicated derivations of prognostic factors, such as whether a subject had ever used a particular concomitant medication for a given length of time, may be harder to identify or document in SDTM-based datasets. If using an Interactive Voice Randomization System (IVRS), the values used for randomization would be captured by the system and would correspond to the values that are represented on the randomization schedule. The “as verified” values are typically derived by comparing the values used for randomization against the data that is in SDTM, whether it be a simple match with a single data point such as sex or the reprogramming of more complex factors such as previous treatments. Table 2.1.1 provides the full set of proposed variables to allow maximum flexibility in representing the description of the prognostic factors, as well as the values used for randomization and the values that were verified. These variables would be found in ADSL. Some metadata have been omitted from the table, either because they are at the sponsor’s discretion (such as the source and manner in which they are derived) or simply to leave more space for the variable descriptions and examples provided in the “CDISC Notes” column. To better illustrate the interrelationships of the variables, the examples are all based on the same



hypothetical situation, in which a trial used three stratification factors of Age Group (“<50” or “ >=50”), Prior Treatment Status (“Treatment naïve”, “Treatment experienced”), and Hypertension (“Y” or “N”). Section 3.1 has another example that is specific to the full Diabetes ADSL example. Table 2.1.1: Proposed Stratification Variables Variable Name Variable Label Type Core CDISC Notes STRATA Randomized

Strata text Cond This entire string value represents the combination of values of the individual stratification factors used for

randomization. This variable is conditional based on whether the trial used stratified randomization. For example, “>=50, Treatment experienced, N”

STRATAN Randomized Strata (N)

integer Perm This is a numeric variable that corresponds to each unique value of STRATA. There must be a 1:1 correspondence between STRATA and STRATAN. For example, STRATAN= “3” when STRATA = “>=50, Treatment experienced, N”

STRATyNM Description of Stratum y

text Perm This is a full text description of the stratification factor “y”. This text description will remain constant for all subjects. These descriptive variables are included to quickly and clearly communicate critical study design information as well as to facilitate integration. This strategy is consistent with other ADaM variables such as CRIT1. For example, STRAT3NM= “Hypertension”

STRATy Randomized Value of Stratum y

text Perm This is the subject-level value of the “y’th” stratification factor and the value used for randomization. For example, STRAT3= “N”

STRATyN Randomized Value of Stratum y (N)

integer Perm This is a numeric variable that corresponds to each unique value of STRATy. There must be a 1:1 correspondence between STRATy and STRATyN. For example, STRAT3N=0 when STRAT3= “N”

STRATAV Verified Strata text Cond This entire string value represents the combination of values of the individual stratification factors that should have been used and were verified after randomization. If the values used for the randomization of a given subject were all correct, then STRATAV will equal STRATA. Otherwise, one or more components of the text string for STRATA and STRATV will be different. This variable is conditional based on whether the trial used stratified randomization and whether differences between the “as randomized” and “as verified” values are important for sensitivity analysis. For example, “>=50, Treatment experienced, Y”

STRATAVN Verified Strata (N) integer Cond This is a numeric variable that corresponds to each unique value of STRATAV. There must be a 1:1 correspondence between STRATAV and STRATAVN. For example, STRATAVN=3 when STRATAV = “>=50, Treatment experienced, N”

STRATyV Verified Value of Stratum y

text Perm This is the “as verified” subject-level value of the “y’th” stratification factor. If the value used for randomization was correct, then STRATyV will equal STRATy. For example, STRAT3V= “Y”

STRATyVN Verified Value of Stratum y (N)

integer Perm This is a numeric variable that corresponds to each unique value of STRATyV. There must be a 1:1 correspondence between STRATyV and STRATyVN. For example, STRAT3VN=1 when STRAT3V= “Y”



2.2 ADSL Example The metadata tables below provide an example of an abbreviated ADSL dataset. Variables that would commonly occur in ADSL regardless of therapeutic area, such as sex, race, age, age groups, geographic region, population flags, treatment assignments, treatment start and stop date, etc. are not shown. The variables presented below are those that may be of specific interest to the analysis of diabetes trial data. Flag variables indicating background medical history events of interest and baseline efficacy laboratory variables are considered optional variables, and only a few have been selected for reference. Because the stratification variables are newly proposed, the example below demonstrates the creation of all of the stratification variables for a hypothetical phase III parallel group design that used two stratification factors at randomization: 1) baseline HbA1c (>7-<9%, ≥9%), and 2) background use of metformin in combination with insulin or metformin alone. Additional details regarding these medications such as brand, formulation, dose, etc., would be included in the medication datasets and not in ADSL unless considered critical for the creation of important patient subgroups. In this example, Subject 001 was randomized into the “>7-<9%” stratum, although the qualifying HbA1c value was 9.3%. The “as randomized” variables remain as “>7-<9%”, while the verified variables are updated to reflect the “≥9%” stratum. Subject 002 was randomized correctly, and therefore has “as randomized” and “verified” strata variables that match. The following tables provide examples of an ADSL analysis dataset (Table 2.2.1), ADSL dataset metadata (Table 2.2.2), and ADSL variable metadata (Table 2.2.3). The source derivation metadata for the variables are provided for illustrative purposes and not intended to represent standard derivation logic. Within the Source/Derivation column is additional text that is meant to provide further discussion for the variable and would not be present in an actual define.xml document. Table 2.2.1: ADSL Analysis Dataset Row STUDYID USUBJID STRATA STRATAN STRAT1NM STRAT1 STRAT1N STRAT2NM STRAT2 STRAT2N

1 XYZ XYZ-001-001 >7-<9% | Metformin alone 1 HbA1c at baseline >7-<9% 0 Background Diabetes Medication at Baseline Metformin alone 0

2 XYZ XYZ-001-002 >=9% | Metformin + insulin 4 HbA1c at baseline >=9% 1 Background Diabetes Medication at Baseline Metformin + insulin 1

Row STRATAV STRAT1V STRAT1VN STRAT2V STRAT2V HBA1CBL HBA1CGR1 DIABCMBL DIABDURY EGFRBL HOMAIRBL

1 (cont) >=9% | Metformin alone >=9% 1 Metformin alone 0 9.3 >=9.0 Metformin alone 4.2 76.3 1.5 2 (cont) >=9% | Metformin + insulin >=9% 1 Metformin + insulin 1 9.1 >=9.0 Metformin + insulin 0.5 87.2 2.3

Row CPEPTBL RETINOFL NEPHROFL HEIGHTBL WEIGHTBL BMIBL BMIGR1

1 (cont) 2.3 Y 157 59.0 23.9 <25 2 (cont) 1.2 Y Y 180 102.4 31.6 >=30

Table 2.2.2: ADSL Dataset Metadata Dataset Name Description Class Structure Purpose Keys Location Documentation ADSL Subject-level Analysis SUBJECT-LEVEL ANALYSIS DATASET One record per subject Analysis STUDYID, USUBJID ADSL.xpt ADSL.SAS

Table 2.2.3: ADSL Variable Metadata

Variable Name Variable Label Type Length/Display

Format Codelist/Controlled Terms Source/Derivation/Comment

STUDYID Study Identifier text $15 DM.STUDYID





USUBJID Unique Subject Identifier

text $15 DM.USUBJID

STRATA Randomized Strata

text $30 >7-<9% | Metformin alone; >=9% | Metformin alone; >7-<9% | Metformin + insulin; >=9% | Metformin + insulin

Obtained from QVAL in SUPPDM where QNAM = “STRATA” Note: At present there is not a standard approach for capturing stratification factors in SDTM-based datasets. This variable represents the combination of individual stratum values used for randomization. The above text is an example and uses a pipe (|) as a delimiter between individual stratum values. These data could come from other sources as well depending on methodologies used for the design and the management of the randomization schedule.

STRATAN Randomized Strata (N)

integer 1 1; 2; 3; 4 = 1 when ADSL.STRATA = “>7-<9% | Metformin alone”; = 2 when ADSL.STRATA = “>=9% | Metformin alone”; = 3 when ADSL.STRATA = “>7-<9% | Metformin + insulin”; = 4 when ADSL.STRATA = “>=9% | Metformin + insulin”

STRAT1NM Description of Stratum 1

text $20 HbA1c at Baseline Assigned based on stratification factors defined a given trial. The value is the same across all subjects and is intended to provide a full text description of the first stratification factor.

STRAT1 Randomized Value of Stratum 1

text $6 >7-<9%; >=9% Derived from ADSL.STRATA and is the text string up to the first delimiter of “,”.

STRAT1N Randomized Value of Stratum 1 (N)

integer 1 0; 1 = 0 when ADSL.STRAT1 = “>7-<9%”; = 1 when ADSL.STRAT1 = “>=9%”

STRAT2NM Description of Stratum 2

text $50 Background Diabetes Medication at Baseline

Assigned based on stratification factors defined for a given trial. The value is the same across all subjects and is intended to provide a full text description of the second stratification factor.

STRAT2 Randomized Value of Stratum 2

text $20 Metformin alone; Metformin + insulin

Derived from ADSL.STRATA and is the text string after the first delimiter of “,”.

STRAT2N Randomized Value of Stratum 2 (N)

integer 1 0; 1 = 0 when ADSL.STRAT2 = “Metformin alone”; = 1 when ADSL.STRAT2 = “Metformin + insulin”;

STRATAV Verified Strata text $30 >7-<9% | Metformin alone; >=9% | Metformin alone; >7-<9% | Metformin + insulin; >=9% | Metformin + insulin

Obtained from QVAL in SUPPDM where QNAM = “STRATAV” Note: There is no standard for if and how the updated STRAT--V are captured and recorded. This is an example of one method and it implies that the full-text string of the concatenated stratum variables has been recorded. These data could come from other sources, such as a programmatic check of CM and LB domains, and will depend on the trial design.

STRATAVN Verified Strata (N)

integer 1 1; 2; 3; 4 = 1 when ADSL.STRATAV = “>7-<9% | Metformin alone”; = 2 when ADSL.STRATAV = “>=9% | Metformin alone”; = 3 when ADSL.STRATAV= “>7-<9% | Metformin + insulin”; = 4 when ADSL.STRATAV = “>=9% | Metformin + insulin”





STRAT1V Verified Value of Stratum 1

text $6 >7-<9%; >=9% Derived from ADSL.STRATAV and is the text string up to the first delimiter of “,”.

STRAT1VN Verified Value of Stratum 1 (N)

integer 1 0; 1 = 0 when ADSL.STRAT1 = “>7-<9%”; = 1 when ADSL.STRAT1 = “>=9%”

STRAT2V Verified Value of Stratum 2


Derived from ADSL.STRATAV and is the text string after the first delimiter of “,”.

STRAT2VN Verified Value of Stratum 2 (N)

integer 1 0; 1 = 0 when ADSL.STRAT2 = “Metformin alone”; = 1 when ADSL.STRAT2 = “Metformin + insulin”;

HBA1CBL HbA1c at Baseline (%)

float 8.1 Value of LB.LBSTRESN where LB.LBTESTCD = “HBA1C” and LB.LBDTC is the closest prior date to DM.RFSTDTC. This represents the value collected just prior to randomization and the value that should have been used to determine the stratification used for randomization.

HBA1CGR1 HbA1c at Baseline (%) Group 1

text $10 >7-<9%; >=9% Categorization of ADSL.HBA1CBL Note: When the accurate value of HbA1c group was used for randomization, this variable will duplicate the information found in STRAT1V and in STRAT1 and the subject was randomized correctly. However in this example, it is considered helpful to have as a separate variable with an explicit variable label.

DIABCMBL Background Diabetes Medication


= “Metformin + insulin” if CM.CMCAT = “DIABETES” and CM.CMTRT = “INSULIN” and CMSTDTC is before or on the first dose date (ADSL.TRTSDT) = “Metformin alone” otherwise This represents the updated stratification value. Note: See note above for HBA1CBL. This variable is similar, yet captures the information pertaining to background medication in a separate variable.

DIABDURY Duration of Diabetes (years)

float 8.1 Difference between ADSL.SCRSDT (screening date) and MH.MHSTDTC where MH.MHTERM = “DIABETES”. See SAP for details regarding imputation of partial dates.

EGFRBL eGFR MDRD (ML/MIN/1.73 M**2)

float 8.1 Value of LB.LBSTRESN where LB.LBTESTCD = “EGRFL” and LB.LBDTC is the closest prior date to DM.RFSTDTC. This represents the value collected just prior to dosing.

HOMAIRBL Baseline HOMA-IR

float 6.2 Value of LB.LBSTRESN where LB.LBTESTCD = “HOMAIR” and LB.LBDTC is the closest prior date to DM.RFSTDTC. This represents the value collected just prior to dosing.

CPEPTBL Baseline C-peptide (ng/mL)

float 6.2 Value of LB.LBSTRESN where LB.LBTESTCD = “CPEPTIDE” and LB.LBDTC is the closest prior date to DM.RFSTDTC. This represents the value collected just prior to dosing.

RETINOFL Medical Hx of Diabetic Retinopathy Flag

text $1 Y = “Y” where MH.MHTERM = 'DIABETIC RETINOPATHY' and MHOCCUR = “Y”

NEPHROFL Medical Hx of Diabetic Nephropathy Flag

text $1 Y = “Y” where MH.MHTERM = “NEPHROPATHY” and MHOCCUR = “Y”





HEIGHTBL Height at Baseline (cm)

integer 8 The last available of VS.VSSTRESN for VS.VSTESTCD = “HEIGHT” before or on the first dose date (ADSL.TRTSDT)

WEIGHTBL Weight at Baseline (kg)

float 8.1 The last available of VS.VSSTRESN for VS.VSTESTCD = “WEIGHT” before or on the first dose date (ADSL.TRTSDT)

BMIBL Body Mass Index at Baseline (kg/m2)

float 8.1 ADSL.WEIGHTBL /ADSL.HEIGHTBL**2

BMIGR1 BMI Group 1 text $10 <25; >=25 - <30; >=30 Categorization of ADSL.BMIBL

3 Analysis of Hypoglycemic Episodes The examples of hypoglycemic data provided in the following sections are based on methodologies widely used throughout clinical research within diabetes. Hypoglycemic episodes are mainly self-reported events where the information is gathered in patient diaries. From there, the data are entered into a hypoglycemia form in the eCRFs. Hypoglycemic events are often summarized and analyzed by American Diabetes Association (ADA) classification (see Seaquist et al1 for details) and each event is classified based on different kinds of information collected from the hypoglycemia form. When glucose concentrations are measured to support classification according to the ADA severity classes, they are represented in SDTM-based datasets using the LB domain alongside any planned glucose measurements (e.g., planned samples evaluated at a central laboratory). It should be noted that measurements of glucose that were collected as part of the hypoglycemia information will usually only be used for the purpose of classifying hypoglycemia (e.g., according to the ADA criteria). There are two abbreviated analysis datasets presented below. The first dataset gathers all information related to hypoglycemic events from the relevant SDTM domains and includes the derived ADA-classification for each event. The second dataset is built from the first dataset and allows for an analysis ready approach to the summarization of hypoglycemic episodes by classification for each subject. Note that hypoglycemic episodes are important events for diabetic patients and presentations of analyses of hypoglycemic episodes are often based on the safety analysis set and actual treatment. However, a reduction in hypoglycemic episodes can also be considered a positive property of an investigational drug; hence the number of hypoglycemic episodes can also be considered an efficacy endpoint, and summarized by planned treatment for various efficacy populations.

3.1 Hypoglycemic Episodes Analysis Dataset In the example analysis dataset ADHYPO shown below (Table 3.1.1), each row represents one hypoglycemic episode. This analysis dataset collates onto one record the pertinent data for each episode that is represented in multiple SDTM domains. Sample data for two subjects are provided, illustrating multiple hypoglycemic events for each subject. The MIDS variable from the CE domain identifies the individual hypoglycemic episode. Details on a given event are mapped from the CE domain CE, in line with the TAUG-Diabetes. A number of variables are ADaM variables that can be transferred directly from ADSL and hence are easily traced back to their respective domains. Further, a number of analysis variables are derived such as ASTDY, the relative analysis day and the traceability for these variables are ensured by the metadata shown in Table 3.1.3. Finally, a number of sponsor defined variables are present, such as SELFTRFL (“was the subject able to self-treat her or himself?” (yes/no)) and LMLRELTM (“last meal relative time”). The first variable mentioned is needed to find the ADA class for each event and the



second variable is needed in the statistical analysis of the hypoglycemic episodes, since events within 2 hours from the last main meal will be analyzed separately. The source/derivation metadata provided below serve as an example of typical metadata and should not be interpreted as precise methods for how these variables should be derived. Summary tables of hypoglycemic episodes can be produced from ADHYPO. Table 3.2.1 shows a summary example presented by arm, with number and percentage of subjects experiencing at least one event together with number of events. Table 3.2.2 presents a summary of events by time. Table 3.1.1: ADHYPO Analysis Dataset Row STUDYID USUBJID MIDS CEDECOD WASAEYN ASTDTM TRTEMFL SELFTRFL SYMPFL NOCTFL GLUCSTD GLUCCONV ASTDY LMLDTM

1 XYZ 000001 HYPO 1 Hypoglycemia Y 07Sep2012 22:29:00 Y N Y N 2.8 52 3 07Sep2012 20:33:00

2 XYZ 000001 HYPO 2 Hypoglycemia N 10Sep2012 09:12:00 Y Y N N 2.6 48 6 10Sep2012 08:15:00

3 XYZ 000001 HYPO 3 Hypoglycemia N 10Sep2012 23:05:00 Y Y Y Y 3.3 60 6 10Sep2012 21:06:00

4 XYZ 000001 HYPO 4 Hypoglycemia N 11Sep2012 15:24:00 Y Y Y N 3.9 71 7 11Sep2012 14:40:00

5 XYZ 000001 HYPO 5 Hypoglycemia N 18Sep2012 11:39:00 Y Y N N 3.9 71 14 18Sep2012 08:27:00

6 XYZ 000002 HYPO 1 Hypoglycemia N 22Oct2012 13:28:00 Y Y N N 3.4 62 6 22Oct2012 09:58:00

7 XYZ 000002 HYPO 2 Hypoglycemia N 25Oct2012 13:59:00 Y Y Y N 2.4 45 9 25Oct2012 10:50:00

8 XYZ 000002 HYPO 3 Hypoglycemia N 17Nov2012 05:01:00 Y N N Y 2.8 51 32 17Nov2012 03:30:00

Row LMLRELTM LMLRELTU LEXDTM LEXRELTM LEXRELTU ASEV ASEVGR1 TRT

A

1 (cont) 116 Minutes 07Sep2012 20:29:00 120 Minutes Severe Hypoglycemia Documented Symptomatic or Severe Hypoglycemia Drug

A


A


A


A


B

6 (cont) 210 Minutes 22Oct2012 09:31:00 237 Minutes Pseudo-Hypoglycemia

Asymptomatic Hypoglycemia, Probable Symptomatic

Hypoglycemia or Pseudo- Hypoglycemia

Drug B

7 (cont) 189 Minutes 25Oct2012 10:29:00 210 Minutes Severe Hypoglycemia Documented Symptomatic or Severe Hypoglycemia Drug

B

8 (cont) 91 Minutes 17Nov2012 03:25:00 96 Minutes Severe Hypoglycemia Documented Symptomatic or Severe Hypoglycemia Drug

B



Table 3.1.2: ADHYPO Dataset Metadata Dataset Name Dataset Description Dataset Location Dataset Structure Keys Class Documentation ADHYPO Hypoglycemic Episodes Analysis Dataset adhypo.xpt One record per subject per event STUDYID, USUBJID, MIDS OCCDS ADHYPO.SAS/SAP

Table 3.1.3: ADHYPO Variable Metadata



STUDYID Study Identifier text $12 ADSL.STUDYID USUBJID Unique Subject

Identifier text $20 ADSL.USUBJID

MIDS Disease Milestone ID text CE.MIDS CEDECOD Dictionary-Derived

Term text CE.CEDECOD

WASAEYN Was This an Adverse Event

text FAORRES where FA.MIDS = CE.MIDS and FATESTCD= “WASAEYN”

ASTDTM Analysis Start Datetime

integer Datetime. Onset of the hypoglycemic episode. Derived based on CE.CESTDTC.

ASTDY Analysis Start Relative Day

integer The number of days from date of first dose until onset of hypoglycemic episode, derived from ASTDTM and ADSL.TRTSDT

TRTEMFL Treatment Emergent Analysis Flag

text $1 Y; N If ADSL.TRTSDT <= ASTDT <= (ADSL.TRTEDT +1) then TRTEMFL = “Y”

SELFTRFL Subject Able to Treat Self Flag

text $1 Y; N FAORRES where FA.MIDS = CE.MIDS and FAOBJ = “HYPOGLYCEMIC EVENT” and FACAT = “TREATMENT ADMINISTRATION” and FATESTCD = “TXASSIST” and FATEST ^= “TREATMENT ASSISTANCE”

SYMPFL Symptomatic Event Flag

text $1 Y; N CEYN where CECAT = “HYPO SYMPTOMS”

NOCTFL Nocturnal Event Flag text $1 Y; N Based on sponsor definition of a nocturnal event (e.g., from midnight until 6:00 in the morning).

GLUCSTD Glucose (mmol/L) at Time of Event

float 8.1 LBSTRESN where LB.MIDS = CE.MIDS AND LBTESTCD="GLUC" where LBSTRESN is converted to standard SI units of “mmol/L” if necessary.

GLUCCONV Glucose (mg/dl) at Time of the Event

float 8.1 LBSTRESN where LB.MIDS = CE.MIDS AND LBTESTCD = “GLUC” where LBSTRESN is converted to conventional units of “mg/dL” if necessary.

LMLDTM Last Meal Datetime integer Datetime. MLSTDTC where ML.MIDS = CE.MIDS AND RELMIDS = “LAST MEAL PRIOR TO HYPO” and MLTRT = “MEAL”

LMLRELTM Time Btwn Last Meal and Hypo Onset

integer 4. Relative time from last meal to onset of hypo (ASTDTM-LMLDTM)

LMLRELTU Time Btwn Last Meal and Hypo Onset Unit

text $7 Minutes Unit of time from last meal to onset of hypo





LEXDTM Last Exposure to Study Drug Datetime

integer Datetime. EXSTDTC where EX.MIDS=CE.MIDS AND RELMIDS = “LAST DOSE PRIOR TO HYPO” and EXCAT = “HIGHLIGHTED DOSE”

LEXRELTM Time Btwn Last Exposure and Hypo Onset

integer 4. Relative time from last exposure to drug to onset of hypo. (ASTDTM-LEXDTM)

LEXRELTU Time Btwn Last Exp and Hypo Onset Unit

text $7 Minutes Unit of time from last exposure to drug to onset of hypo unit

ASEV Analysis Severity/Intensity

text $22 Severe Hypoglycemia; Documented Symptomatic Hypoglycemia; Asymptomatic Hypoglycemia; Probable Symptomatic Hypoglycemia; Pseudo-Hypoglycemia

Based on ADA Classification: Severe Hypoglycemia/ Documented Symptomatic Hypoglycemia/ Asymptomatic Hypoglycemia/ Probable Symptomatic Hypoglycemia/ Pseudo-hypoglycemia

ASEVGR1 Pooled Severity Group 1

text $45 Documented Symptomatic or Severe Hypoglycemia; Asymptomatic Hypoglycemia, Probable Symptomatic Hypoglycemia or Pseudo-Hypoglycemia

Categorization based on values of ASEV. In this example, the categories are “Documented Symptomatic or Severe Hypoglycemia” and “Asymptomatic, Probable Symptomatic Hypoglycemia, and Pseudo-Hypoglycemia”

TRTA Actual Treatment text $32 ADSL.TRT01A

3.2 Hypoglycemic Episodes Analysis Results A first presentation of the hypoglycemic episodes will often be a summary table, where the number of total events is presented together with the number and percentage of subjects with events. An example of the simple summary table is shown in Table 3.2.1, which presents hypoglycemic episodes that occur within two hours since the last main meal – split by diurnal and nocturnal and by severity. The events can also be summarized by time (using the ADY variable) in the trial, as shown in Table 3.2.2. For these two table examples, analysis results metadata are not presented. Table 3.2.1: Summary of Post-Meal Hypoglycemic Episodes by Severity – Table Shell

Hypoglycemic episodes within 2 hours since last meal by severity Summary – Safety Analysis Set

Drug A Drug B N (%) E N (%) E

Number of subjects xxx xx

Diurnal xxx (xx.x) xxx xx (xx.x) xxx Documented Symptomatic xx (xx.x) xx xx (xx.x) xx Pseudo Symptomatic xx (xx.x) xx xx (xx.x) xx Probable Symptomatic x (xx.x) xx x ( x.x) x

Nocturnal x ( x.x) x x ( x.x) x Documented Symptomatic x ( x.x) x x Probable Symptomatic x xx ( x.x) x

N: Number of subjects; %: Percentage of subjects; E: Number of events



Table 3.2.2: Summary of Hypoglycemic Episodes by Classification and Time – Table Shell Hypoglycemic Episodes by Classification and Time – Summary – Safety Analysis Set

Drug A Drug B Total N (%) E N (%) E N (%) E

Number of Subjects xxx xxx xxx

Pseudo Symptomatic x ( x.x) x x ( x.x) x x ( x.x) x Week 1 x ( x.x) x x ( x.x) x x ( x.x) x Week 2 x ( x.x) x x ( x.x) x x ( x.x) x End of treatment x ( x.x) x x ( x.x) x x ( x.x) x

Documented Symptomatic

xx (xx.x) xx xx (xx.x) xx xx (xx.x) xxx

Week 1 x ( x.x) xx x ( x.x) xx xx ( x.x) xx Week 2 x ( x.x) x x ( x.x) xx x ( x.x) xx End of treatment xx ( x.x) xx x ( x.x) xx xx ( x.x) xx

N: Number of subjects; %: Percentage of subjects; E: Number of events

3.3 Hypoglycemic Episodes Summary Dataset The analysis dataset ADHYSUM is built from an ADHYPO data set and supports both the statistical analysis of the hypoglycemic events and the tabular summary of frequencies of hypoglycemic episodes (see Table 3.3.1). The dataset includes one observation per combination of subject, analysis parameter, time window and indicator (e.g., treatment emergent flag). Each record is a summary of the type of hypoglycemic episode described by the parameter, per subject. For each combination of parameter and the timing variable, AVISIT, records are created even if no hypoglycemic episodes occurred. The statistical model presented below is based on the actual treatment received (TRTA) and adjusted for subject-level values of country and sex. Therefore, these variables are included in ADHYSUM from ADSL to support analysis readiness. The duration of exposure (TRTDURD) is added to the dataset in order to facilitate exposure adjusted incidence rates. For overall summaries the records which have “cumulative frequency count” within the text of PARAM and AVISIT = “End of treatment” can be selected. In this example, parameters for each of the five ADA classification values are defined, along with a derived parameter that represents a grouping of two of the classification values (documented symptomatic or severe hypoglycemia). Mock data for this summary dataset is provided below in Table 3.3.1, yet this mock data shows only a subset of the possible values of analysis parameters. The examples below do not attempt to show all the data needed fully visualize the traceability between ADHYPO and ADHYSUM for a given subject since the volume of required mock data would be large,. In practice, however, the counts derived in ADHYSUM for a given subject would be completely traceable to the counts of individual rows for that subject found in the source ADHYPO dataset. Table 3.3.1: ADHYSUM Analysis Dataset Row STUDYID USUBJID PARAMCD PARAM AVISIT AVAL TRTDURD SEX AGE COUNTRY TRTA

1 XYZ 000008 ASSYMP Asymptomatic Hypoglycemia (frequency count) Week 1 3 72 F 35 DZA Drug B 2 XYZ 000008 ASSYMPC Asymptomatic Hypoglycemia (cumulative frequency count) Week 1 3 72 F 35 DZA Drug B 3 XYZ 000008 ASSYMP Asymptomatic Hypoglycemia (frequency count) Week 2 1 72 F 35 DZA Drug B 4 XYZ 000008 ASSYMPC Asymptomatic Hypoglycemia (cumulative frequency count) Week 2 4 72 F 35 DZA Drug B 5 XYZ 000008 ASSYMP Asymptomatic Hypoglycemia (frequency count) Week 3 0 72 F 35 DZA Drug B 6 XYZ 000008 ASSYMPC Asymptomatic Hypoglycemia (cumulative frequency count) Week 3 4 72 F 35 DZA Drug B 7 XYZ 000008 ASSYMP Asymptomatic Hypoglycemia (frequency count) Week 4 1 72 F 35 DZA Drug B 8 XYZ 000008 ASSYMPC Asymptomatic Hypoglycemia (cumulative frequency count) Week 4 5 72 F 35 DZA Drug B

10 XYZ 000008 ASSYMPC Asymptomatic Hypoglycemia (cumulative frequency count) End of Treatment 7 72 F 35 DZA Drug B



Row STUDYID USUBJID PARAMCD PARAM AVISIT AVAL TRTDURD SEX AGE COUNTRY TRTA … … … … … … … … … … … … 20 XYZ 000008 DOCSEVC Documented Symptomatic or Severe Hypoglycemia (cumulative frequency count) End of Treatment 17 72 F 35 DZA Drug B

Table 3.3.2: ADHYSUM Dataset Metadata Dataset Name Dataset Description Dataset Location Dataset Structure Keys Class Documentation ADHYSUM Hypoglycemic Episodes

Summary Data ADHYSUM.xpt One record per subject per analysis visit

per parameter STUDYID, USUBJID, AVISIT, PARAMCD

BDS ADHYSUM.SAS/SAP

Table 3.3.3: ADHYSUM Variable Metadata



STUDYID Study Identifier text $12 ADSL.STUDYID USUBJID Unique

Subject Identifier text $20 ADSL.USUBJID

PARAMCD Parameter Code text $8 See parameter value metadata. Note that the tables below do not present all possible values for PARAMCD but only those that correspond to the data display.

PARAM Parameter text $80 See parameter value metadata. Note that the tables below do not present all possible values for PARAM but only those that correspond to the data display.

AVISIT Analysis Visit text $13 Week -1; Week 0; Week 1; Week N; End of Treatment

Refer to Section X.X of the SAP for windowing and imputation algorithms based on ADHYPO.ADY. End-of-treatment is defined as the last week during which the subject is on treatment.

AVAL Analysis Value integer 8 See parameter value metadata. TRTDURD Total Treatment

Duration (Days) integer 8 ADSL.TRTDURD

SEX Sex text $1 ADSL.SEX AGE Age integer 8 ADSL.AGE COUNTRY Country text $3 ADSL.COUNTRY TRTA Actual Treatment text $32 ADSL.TRT01A

Table 3.3.4: ADHYSUM Parameter [CL.PARAM. ADHYSUM] Permitted Value (Code) Asymptomatic Hypoglycemia (frequency count) Asymptomatic Hypoglycemia (cumulative frequency count) Probable Symptomatic Hypoglycemia (frequency count) Probable Symptomatic Hypoglycemia (cumulative frequency count) Pseudo-Hypoglycemia (frequency count) Pseudo-Hypoglycemia (cumulative frequency count) Documented Symptomatic (frequency count) Documented Symptomatic (cumulative frequency count)



Permitted Value (Code) Severe Hypoglycemia (frequency count) Severe Hypoglycemia (cumulative frequency count) Documented Symptomatic or Severe Hypoglycemia (frequency count) Documented Symptomatic or Severe Hypoglycemia (cumulative frequency count)

Table 3.3.5: ADHYSUM Parameter Code [CL.PARAMCD. ADHYSUM] Permitted Value (Code) Display Value (Decode) ASSYMP Asymptomatic Hypoglycemia (frequency count) ASSYMPC Asymptomatic Hypoglycemia (cumulative frequency count) PROBAB Probable Symptomatic Hypoglycemia (frequency count) PROBABC Probable Symptomatic Hypoglycemia (cumulative frequency count) PSEUDO Pseudo-Hypoglycemia (frequency count) PSEUDOC Pseudo-Hypoglycemia (cumulative frequency count) DOCUMEN Documented Symptomatic (frequency count) DOCUMENC Documented Symptomatic (cumulative frequency count) SEVHYPO Severe Hypoglycemia (frequency count) SEVHYPOC Severe Hypoglycemia (cumulative frequency count) DOCSEV Documented Symptomatic or Severe Hypoglycemia (frequency count) DOCSEVC Documented Symptomatic or Severe Hypoglycemia (cumulative frequency count)

Table 3.3.6: Parameter Value-Level List – ADHYSUM [AVAL]

Variable Where Type Length/ Display Format

Codelist/ Controlled

Terms Source/Derivation/Comment

AVAL PARAMCD = “ASSYMP” integer 3. Derived: AVAL equals the number of events in ADHYPO that occur during the period defined by AVISIT and have a value of ASEV of “Asymptomatic Hypoglycemia”.

AVAL PARAMCD = “ASSYMPC” integer 3. Derived: AVAL equals the number of asymptomatic hypoglycemic events that have occurred from the beginning of the trial up to AVISIT. It is equal to the sum of all values of AVAL from all records in ADHYSUM for a given value of AVISIT where PARAMCD = “ASSYMP”.

AVAL PARAMCD = “PROBAB” integer 3. Derived: AVAL equals the number of events in ADHYPO that occur during the period defined by AVISIT and have a value of ASEV of “Probable Symptomatic Hypoglycemia”.

AVAL PARAMCD = “PROBABC” integer 3. Derived: AVAL equals the number of probable symptomatic hypoglycemic events that have occurred from the beginning of the trial up to AVISIT. It is equal to the sum of all values of AVAL from all records in ADHYSUM for a given value of AVISIT where PARAMCD = “PROBAB”.

AVAL PARAMCD = “ PSEUDO” integer 3. Derived: AVAL equals the number of events in ADHYPO that occur during the period defined by AVISIT and have a value of ASEV of “Pseudo-hypoglycemia”.

AVAL PARAMCD = “ PSEUDOC” integer 3. Derived: AVAL equals the number of pseudo-symptomatic hypoglycemic events that have occurred from the beginning of the trial up to AVISIT. It is equal to the sum of all values of AVAL from all records in ADHYSUM for a given value of AVISIT where PARAMCD = “PSEUDO”.

AVAL PARAMCD = “DOCUMEN” integer 3. Derived: AVAL equals the number of events in ADHYPO that occur during the period defined by AVISIT and have a value of ASEV of “Documented Symptomatic Hypoglycemia”.






AVAL PARAMCD = “DOCUMENC” integer 3. Derived: AVAL equals the number of pseudo-symptomatic hypoglycemic events that have occurred from the beginning of the trial up to AVISIT. It is equal to the sum of all values of AVAL from all records in ADHYSUM for a given value of AVISIT where PARAMCD = “DOCUMEN”.

AVAL PARAMCD = “ SEVHYPO” integer 3. Derived: AVAL equals the number of events in ADHYPO that occur during the period defined by AVISIT and have a value of ASEV of “Severe Hypoglycemia”.

AVAL PARAMCD = “SEVHYPOC” integer 3. Derived: AVAL equals the number of severe hypoglycemic events that have occurred from the beginning of the trial up to AVISIT. It is equal to the sum of all values of AVAL from all records in ADHYSUM for a given value of AVISIT where PARAMCD = “SEVHYPO”.

AVAL PARAMCD = “ DOCSEV” integer 3. Derived: AVAL equals the number of records in ADHYPO that occur during the period defined by AVISIT and have a value of ASEVGR1 of “Documented Symptomatic or Severe Hypoglycemia”.

AVAL PARAMCD = “DOCSEVC” integer 3. Derived: AVAL equals the number of records in ADHYPO that occur during the period defined by AVISIT and have a value of ASEVGR1 of “Documented Symptomatic or Severe Hypoglycemia.”

3.4 Hypoglycemic Episodes Summary Analysis Results The summary statistics in Table 3.4.1 are presented for all hypoglycemic episodes as well as by ADA classification group. The statistics presented in the current example are number of subjects experiencing an event, the number of events, and the raw event rate. To estimate and present the event-rate information, exposure time is needed. Table 3.4.1 is based on the ADHYSUM dataset. Table 3.4.1: Summary of Hypoglycemic Episodes by Classification – Table Shell

Hypoglycemic Episodes by Classification – Treatment Emergent – Summary – Safety Analysis Set Drug A Drug B Total N (%) E R N (%) E R N (%) E R

Number of subjects xxx xxx xxx

Total events xx ( xx.x) xx xxx.x xx ( xx.x) xx xxx.x xx ( xx.x) xxx xxx.x

ADA Severe hypoglycemia x ( x.x) x xx.x x ( x.x) x x.x x ( x.x) x x.x Documented symptomatic hypoglycemia xx ( xx.x) xx xxx.x xx ( xx.x) xx xxx.x xx ( xx.x) xxx xxx.x Asymptomatic hypoglycemia x ( x.x) xx xx.x x ( x.x) x xx.x xx ( x.x) xx xx.x Probable symptomatic hypoglycemia x ( x.x) x x.x x ( x.x) x x.x x ( x.x) x x.x Pseudo-hypoglycemia x x x

N: Number of subjects; %: Percentage of subjects; E: Number of events; R: Event rate per 100 exposure years; Severe: Subject unable to treat himself/herself and/or have a recorded PG < 3.1 mmol/L (56 mg/dL) Treatment emergent episodes occur after trial product administration after randomization and no later than 1 day after last trial product administration.

The hypoglycemic episodes can also be summarized by concomitant medication group (e.g., with or without metformin), time since last meal (e.g., within 1 hour of last meal), or other relevant categorical variables.



The event rate over time since randomization for hypoglycemic episodes can be presented graphically by a mean cumulative function plot. In Figure 3.4.1 the severe and documented symptomatic events are compared between the treatment arms. The figure is created based on the cumulative episodes by subjects over time, found in the ADHYSUM dataset.

Figure 3.4.1: Mean Cumulative Function Plot of Documented and Severe Symptomatic Hypoglycemic Episodes Documented and Severe Symptomatic Hypoglycemic Episodes – Treatment Emergent - Mean Cumulative Function - Safety Analysis Set

Different approaches can be followed for the statistical analysis of hypoglycemic episodes. The negative binomial regression, Poisson regression, and several zero-inflated models are evaluated in Bulsara et al2 and Aschner et al3. In Table 3.4.2, the documented symptomatic or severe hypoglycemic episodes are modelled by a negative binomial distribution and compared between the treatment arms. The predicted population mean rates (LSMeans) and the estimated rate ratios between treatment arms are presented. The analysis is based on ADHYSUM and the result metadata are presented in Table 3.4.3.

Num

ber o

f Epi

sode

s per

Sub

ject

0.00

0.04

0.08

0.12

0.16

0.20

0.24

0.28

0.32

0.36

0.40

Time since Randomisation (Weeks) 0 4 8 12 16 20 24 28 32 36 40 44 48 52

Num

ber of Episodes per Subject

0.00

0.04

0.08

0.12

0.16

0.20

0.24

0.28

0.32

0.36

0.40

Drug A

Drug B



Table 3.4.2: Hypoglycemic Episodes – Full Analysis Set Hypoglycemic Episodes – Treatment Emergent – Statistical Analysis – Full Analysis Set

FAS N Estimate 95% CI p

Documented Symptomatic or Severe Hypoglycemic Episodes

LSMeans, Events per 100 PYE

Drug A 197 195 140.97 Drug B 183 183 171.91

Treatment Ratio

Drug A / Drug B 0.82 [ 0.64 ; 1.04] 0.15

N: Number of subjects contributing to analysis; CI: Confidence Interval; PYE: Patient Years Exposure The number of events is analyzed using a Negative Binomial Regression model using a log link and the logarithm of the exposure time (100 years) as offset. The model includes treatment and sex as fixed effects, and age as covariate.

Table 3.4.3: Hypoglycemic Events Analysis Results Metadata Metadata Field Metadata DISPLAY IDENTIFIER Table 3.4.2 DISPLAY NAME Statistical analysis by negative binomial model of severe and documented symptomatic hypoglycemic episodes, by ADA classification RESULT IDENTIFIER Sum of severe and documented symptomatic hypoglycemic events PARAM Documented Symptomatic or Severe Hypoglycemia (cumulative frequency count) PARAMCD DOCSEVC ANALYSIS VARIABLE AVAL ANALYSIS REASON Confirmatory secondary endpoint, as pre-specified in the protocol ANALYSIS DATASET ADHYSUM SELECTION CRITERIA FASFL ="Y" and AVISIT =”End of Treatment” DOCUMENTATION Protocol section x.x: The number of documented symptomatic or severe hypoglycemic episodes will be analyzed based on the Full Analysis Set using

a negative binomial regression model with a log-link function, and the logarithm of the time period in which a hypoglycemic episode is considered treatment emergent as offset. The model will include treatment and sex as factors and age as covariate.

PROGRAMMING STATEMENTS

proc genmod data=adhysum; model AVAL = trtp sex age / dist=nb link=log offset=log(trtdurd); run;



4 Analysis of Glycated Hemoglobin There are a number of derived statistical endpoints and analysis methods that might be used for the analysis of the continuous clinical endpoint of HbA1c. The examples below serve to demonstrate the use of the ADaM standard to create an analysis dataset to support two typical endpoints. This example is based on a Phase III, parallel-group study designed to determine efficacy of Drug A for patients with Type II diabetes. The primary endpoint was defined as the change in HbA1c from baseline. This was analyzed using observed data with a longitudinal repeated measures analysis, including the fixed categorical effects of treatment, week, baseline-by-week, and treatment-by-week interaction, as well as the continuous fixed covariate baseline HbA1c. A secondary endpoint was defined as the proportion of subjects who experienced one or more instances of HbA1c < 7%. This categorical data was analyzed using chi-square tests with the use of exact tests as appropriate. The ADaM dataset below demonstrates the use of the Basic Data Structure (BDS) for both endpoints described above using one analysis parameter for the continuous HbA1c measure. This example also includes the variable DTYPE to show how data for missed visits could be imputed, but it was added for demonstration purposes only, and was not used in the specified analysis examples.

4.1 HbA1c Analysis Dataset The following tables provide examples of a BDS-structured dataset (Table 4.1.1), analysis dataset metadata (Table 4.1.2), and analysis variable metadata (Table 4.1.3) for HbA1c analyzed as a continuous variable and separately as a categorical variable. Note that only selected variables have been shown below; individual trials may require the use of additional or other variables, such as age of onset of diabetes (years) or baseline fasting glucose. Table 4.1.1: ADHBA1C Analysis Dataset Row STUDYID USUBJID PARAM PARAMCD VISIT AVISIT AWTARGET ADY TRTP ITTFL ABLFL BASE AVAL CHG ANL01FL CRIT1 CRIT1FL DTYPE LBSEQ

1 XYZ XYZ-001-001 HbA1c (%) HBA1C Visit 2 Baseline 1 1 Drug A Y Y 9.2 9.2 Y <7% N 23456 2 XYZ XYZ-001-001 HbA1c (%) HBA1C Visit 3 Week 4 28 28 Drug A Y 9.2 8.5 -0.7 Y <7% N 45325 3 XYZ XYZ-001-001 HbA1c (%) HBA1C Visit 4 Week 8 56 56 Drug A Y 9.2 7.3 -1.9 Y <7% N 24768 4 XYZ XYZ-001-001 HbA1c (%) HBA1C Visit 5 Week 12 84 84 Drug A Y 9.2 6.8 -2.4 Y <7% Y 76553 5 XYZ XYZ-001-001 HbA1c (%) HBA1C Visit 6 Week 24 168 168 Drug A Y 9.2 6.3 -2.9 Y <7% Y 65678 6 XYZ XYZ-001-002 HbA1c (%) HBA1C Visit 2 Baseline 1 1 Drug B Y Y 8.6 8.6 Y <7% N 90874 7 XYZ XYZ-001-002 HbA1c (%) HBA1C Visit 3 Week 4 28 28 Drug B Y 8.6 8.7 0.1 Y <7% N 23454 8 XYZ XYZ-001-002 HbA1c (%) HBA1C Visit 4 Week 8 56 56 Drug B Y 8.6 9.6 1.0 Y <7% N 56744 9 XYZ XYZ-001-002 HbA1c (%) HBA1C Visit 5 Week 8 56 61 Drug B Y 8.6 9.5 1.1 <7% N 67543

10 XYZ XYZ-001-002 HbA1c (%) HBA1C Visit 5.1 Week 12 84 84 Drug B Y 8.6 9.5 1.1 Y <7% N LOCF 67543 11 XYZ XYZ-001-002 HbA1c (%) HBA1C Visit 6 Week 24 168 168 Drug B Y 8.6 9.5 1.1 Y <7% N LOCF 67543

Table 4.1.2: ADHBA1C Analysis Dataset Metadata

Dataset Description Class Structure Purpose Keys Location Documentation ADHBA1C HbA1c Analysis

Data Basic Data Structure

One record per subject per parameter per analysis visit and day

Analysis STUDYID, USUBJID, PARAMCD, AVISIT, ADY

ADHBA1C.xpt ADHBA1C.SAS/SAP



Table 4.1.3: ADHBA1C Analysis Variable Metadata Variable

Name Variable Label Type Length/Display Format Codelist/Controlled Terms

Source/Derivation/Comment STUDYID Study Identifier text 3 ADSL.STUDYID USUBJID Unique Subject

Identifier text 20 ADSL.USUBJID

PARAM Parameter text 32 HbA1c (%) Populated with ”HbA1c (%)” for records corresponding to HbA1c (LB.LBTESTCD = “HBA1C”)

PARAMCD Parameter Code text 8 HBA1C Populated with ”HBA1C” (based on LB.LBTESTCD = “HBA1C”) VISIT Visit Name text 20 Visit 2; Visit 3; Visit 4;

Visit 5; Visit 5.1; Visit 6 LB.VISIT

AVISIT Analysis Visit text 11 Baseline; Week 4; Week 8; Week 12; Week 24

Refer to Section X.X of the SAP for windowing algorithm based on ADHBA1C.ADY. Baseline visit is defined as the last available value prior to randomization.

AWTARGET Analysis Window Target

integer 3 Refer to Section X.X of the SAP for windowing algorithm.

ADY Analysis Relative Day

integer 3 Refer to Section X.X of the SAP for windowing algorithm based on ADHBA1C.ADY.

TRTP Planned Treatment

text 15 Drug A; Drug B ADSL.TRT01P

ITTFL Intent-To-Treat Population Flag

text 1 Y; N ADSL.ITTFL

ABLFL Baseline Record Flag

text 1 Y Set to “Y” when HBA1C.AVISIT = “Baseline”. See SAP for visit windowing.

BASE Baseline Value float 8.1 BASE = ADHBA1C.AVAL where ADHBA1C.ABLFL = “Y” AVAL Analysis Value float 8.1 AVAL = LB.LBSTRESN where LB.LBTESTCD =”HBA1C” CHG Change from

Baseline float 8.1 Y CHG = AVAL-BASE

ANL01FL Analysis Record Flag 01

text 1 Y Populate with “Y” to identify the record selected to be analyzed for the specific value of AVISIT (already populated based on the analysis window algorithm defined in SAP X.X). If there are multiple records for a value of AVISIT, use the record that is closest to ADHBA1C.AWTARGET.

CRIT1 Analysis Criterion 1

text 50 <7% Populated with ”<7%”

CRIT1FL Criterion 1 Evaluation Result Flag

text 1 Y; N Set to “Y” when ADHBA1C.AVAL<7%. Set to “N” otherwise.

DTYPE Derivation Type text 10 LOCF Set to “LOCF” when ADHBA1C.AVAL is imputed using last observation carried forward (post-baseline only).

LBSEQ Sequence Number integer 4 LB.LBSEQ from the record in the SDTM LB domain containing AVAL.



4.2 HbA1c Analysis Results The HbA1c results are presented using a repeated measures model in Section 4.2.1 and a categorical analysis in Section 4.2.2. Within each section, example output is presented first, followed by the analysis results metadata.

4.2.1 Longitudinal Repeated Measures Model Table 4.2.1: HbA1c Longitudinal Repeated Measures Analysis - Table Shell Protocol: XYZ Page 1 of 2

HbA1c (%) Longitudinal Repeated Measures Analysis 24-Week Short-term Double-blind Treatment Period

Intention-to-treat Population Drug A

N=125 Drug B

N=125 BASELINE N# 125 125

Mean (SD) X.XX( X.XXX) X.XX ( X.XXX)

WEEK 4 N# XXX XXX Change from baseline: Mean (SD) X.XX ( X.XXX) X.XX ( X.XXX) Adjusted change from baseline: Mean (SD) X.XX ( X.XXX) X.XX ( X.XXX) 95% Confidence interval for adjusted mean (XX.XX, XX.X) (XX.XX, XX.X) Difference vs. Drug B (SE) XX.XX ( X.XXXX) 95% Confidence interval for difference (XX.XX, XX.X) P-value vs. Drug B X.XXXX ...

WEEK 12 N# X.XX( X.XXX) X.XX ( X.XXX) Change from baseline: Mean (SD) XXX XXX Adjusted change from baseline: Mean (SD) X.XX ( X.XXX) X.XX ( X.XXX) 95% Confidence interval for adjusted mean X.XX ( X.XXX) X.XX ( X.XXX) Difference vs. Drug B (SE) (XX.XX, XX.X) (XX.XX, XX.X) 95% Confidence interval for difference XX.XX ( X.XXXX) P-value vs. Drug B (XX.XX, XX.X) X.XXXX N: the number of subjects in the Intention-to-treat (ITT) Population. N#: the number of subjects in the ITT population with non-missing baseline and non-missing Week t value. Repeated measures model: change = baseline treatment visit visit*treatment Program Source: xxxxxxxx\xxxx\xxxx\t-hba1c-repmeas.sas <date>:<time>



Figure 4.2.1: Mean Change from Baseline in HbA1c (%) Over Time – Figure Shell Protocol: XYZ Page 2 of 2

Mean Change from Baseline in HbA1c (%) Over Time 24-Week Short-term Double-blind Treatment Period

Intention-to-treat Population

Repeated measures model: change = baseline treatment visit visit*base visit*treatment Mean changes from baseline are based on adjusted changes from baseline from the repeated measure model.

Program Source: /xxxxx/xxxx/xxxx/t-hba1c-repmeas.sas <date>:<time>

Table 4.2.2: HbA1c Longitudinal Repeated Measures Analysis Results Metadata Metadata Field Metadata DISPLAY IDENTIFIER Table 4.2.1/Figure 4.2.1 DISPLAY NAME Mean Change from Baseline in HbA1c (Percent) Longitudinal Repeated Measures Analysis, 24-Week Short-term Double-blind Treatment

Period, Intention-to-treat Population RESULT IDENTIFIER Treatment difference results (LSMean, confidence interval, p-value) PARAM HbA1c (%) PARAMCD HBA1C ANALYSIS VARIABLE CHG (Change from baseline) ANALYSIS REASON SPECIFIED IN SAP ANALYSIS PURPOSE PRIMARY OUTCOME MEASURE ANALYSIS DATASET ADHBA1C

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

Week 4 Week 8 Week 12 Week 24

HbA

1c (%

) Mea

n C

hang

e fr

om B

asel

ine

Drug ADrug B

325 325 322 320324 324 319 318



Metadata Field Metadata SELECTION CRITERIA ITTFL= “Y” and PARAMCD = “HBA1C” and CHG ne . and ANL01FL = “Y” and DTYPE = “ ” DOCUMENTATION See SAP Section XX for details. Program: t-hba1c-repmeas.sas LS means and 95% CIs are based on repeated measures model adjusting for

planned treatment, baseline HbA1c value, avisit, avisit*baseline and avisit*treatment interaction. PROGRAMMING STATEMENTS [SAS version 9.2]

PROC MIXED DATA = ADHBA1C; WHERE ITTFL = “Y” and PARAMCD = “HBA1C” and CHG ne . and ANL01FL = “Y” and DTYPE = “ ” CLASS TRTP AVISIT; MODEL CHG = TRTP BASE AVISIT BASE*AVISIT AVISIT*TRTP / DDFM=KR; LSMEANS TRTP / CL DIFF; REPEATED usubjid / subject = USUBJID TYPE=UN; RUN ;

4.2.2 Categorical Analysis Table 4.2.3: HbA1c Categorical Analysis Table Shell Protocol: XYZ Page 1 of 1

Proportion of Subjects with HbA1C < 7% Intention-to-treat Population

Drug A (N=XXX)

Drug B (N=XXX)

Week 4 X/N# x/xxx x/xxx Percent x.x% x/x% P-value vs. Drug B x.xxxx

...

WEEK 12 X/N# x/xxx x/xxx Percent x.x% x/x% P-value vs. Drug B x.xxxx

N: number of subjects in the ITT analysis set; N#: number of subjects in the ITT analysis set with non-missing baseline and non-missing Week t values; X: number of subjects with HbA1c <7%. P-value is based on a chi-square test. In case of less than 5 events per treatment group, the exact method is used. Program Source: /xxxxx/xxxx/xxxx/t-hba1c-cat.sas <date>:<time>

Table 4.2.4: HbA1c Categorical Analysis Results Metadata Metadata Field Metadata DISPLAY IDENTIFIER Table 4.2.3 DISPLAY NAME Proportion of Subjects with HbA1c <7%, Intention-to-treat Population RESULT IDENTIFIER Treatment proportions and p-values PARAM HbA1C (%) PARAMCD HBA1C ANALYSIS VARIABLE CRIT1



Metadata Field Metadata ANALYSIS REASON SPECIFIED IN SAP ANALYSIS PURPOSE SECONDARY OUTCOME MEASURE ANALYSIS DATASET ADHBA1C SELECTION CRITERIA PARAMCD = “HBA1C” and ITTFL = “Y” and ANL01FL = “Y” and CHG ne . and DTYPE = “ ” DOCUMENTATION See SAP Section XX for details. Program: t-hba1c-cat.sas. P-value based on chi-square test. In case of <5 events per treatment group, the exact

method is used. No imputation method is applied and only subjects with baseline and week t data are included. PROGRAMMING STATEMENTS

[SAS version 9.2] PROC FREQ DATA = ADHBA1C; WHERE PARAMCD = “HBA1C” and ITTFL = “Y” and ANL01FL =”Y” and CHG ne . and DTYPE = “ ”; BY AVISIT; TABLES TRTP*CRIT1FL/ CHISQ EXACT; RUN;

5 Analysis of Glucose Levels It is important in the treatment of diabetes that glucose levels remain controlled both in the short term and long term. Several methodologies can be used to collect and analyze glucose data in order to measure this level of control, such as laboratory fasting glucose, self-monitored blood glucose, and glucose tolerance testing. Fasting glucose is measured in a laboratory setting at regular clinical visits over time. Self-monitored blood glucose (SMBG) is measured by the patient multiple times during the course of normal daily activities using a device such as a glucose meter, and data are recorded by the device or manually entered into a diary. SMBG can also sometimes be referred to as SMPG (self-monitoring plasma glucose) if the glucose meters reports blood glucose values as plasma glucose. For the purposes of the remainder of this document, the terms SMBG and SMPG are treated as interchangeable. Glucose tolerance testing (e.g., oral glucose tolerance test (OGTT), intravenous glucose tolerance test (IVGTT), mixed-meal tolerance test (MMTT)) can also be used to measure glucose control in conjunction with a meal or other glucose source. Another analysis concept described in this section is glucose excursion, or the change in glucose from a constant time point to another targeted time point (e.g., from 0 to 180 minutes, from 0 to 240 minutes).

5.1 Self-Monitored Glucose Profile Analysis Dataset This example is based on a Phase III, parallel-group study designed to determine efficacy of Drug A for patients with Type II diabetes. While the choice of model may vary, for this example, the SMBG data are analyzed using a longitudinal repeated measures analysis including the fixed categorical effects of treatment, week and treatment-by-week interaction as well as the continuous fixed covariate baseline. The following tables provide examples of a Basic Data Structure (BDS) analysis dataset, and analysis-variable metadata for glucose analyzed as a continuous variable. Note that some important variables may not be presented, as only selected variables were chosen to focus on the most important concepts. First, consider the simplified data in the SDTM LB dataset below. It shows two visits with complete 9-point SMBG in a 24-hour period. In a more realistic case there would be more than one day of measurements per visit.



For analysis, a single representative visit value was derived for each of the 9 time points using some algorithm (typically a mean value). Table 5.1.1: Glucose excerpt from SDTM LB domain corresponding to SMBG readings Row STUDYID DOMAIN USUBJID LBSEQ LBTESTCD LBTEST LBCAT LBSTRESN LBSTRESU LBSTAT LBSPEC LBBLFL LBFAST VISITNUM VISIT

1 XYZ LB XYZ-1-002 1 GLUC Glucose CHEMISTRY 6.8 mmol/L PLASMA Y Y 2 VISIT2 2 XYZ LB XYZ-1-002 2 GLUC Glucose CHEMISTRY 8.9 mmol/L PLASMA Y 2 VISIT2 3 XYZ LB XYZ-1-002 3 GLUC Glucose CHEMISTRY 7.3 mmol/L PLASMA Y 2 VISIT2 4 XYZ LB XYZ-1-002 4 GLUC Glucose CHEMISTRY 8.5 mmol/L PLASMA Y 2 VISIT2 5 XYZ LB XYZ-1-002 5 GLUC Glucose CHEMISTRY 7.9 mmol/L PLASMA Y 2 VISIT2 6 XYZ LB XYZ-1-002 6 GLUC Glucose CHEMISTRY 8.1 mmol/L PLASMA Y 2 VISIT2 7 XYZ LB XYZ-1-002 7 GLUC Glucose CHEMISTRY 19.1 mmol/L PLASMA Y 2 VISIT2 8 XYZ LB XYZ-1-002 8 GLUC Glucose CHEMISTRY 6.4 mmol/L PLASMA Y 2 VISIT2 9 XYZ LB XYZ-1-002 9 GLUC Glucose CHEMISTRY 6.6 mmol/L PLASMA Y Y 2 VISIT2

10 XYZ LB XYZ-1-002 10 GLUC Glucose CHEMISTRY 6.5 mmol/L PLASMA Y 4 VISIT4

11 XYZ LB XYZ-1-002 11 GLUC Glucose CHEMISTRY NOT DONE PLASMA 4 VISIT4

12 XYZ LB XYZ-1-002 12 GLUC Glucose CHEMISTRY 6.3 mmol/L PLASMA 4 VISIT4 13 XYZ LB XYZ-1-002 13 GLUC Glucose CHEMISTRY 9.1 mmol/L PLASMA 4 VISIT4 14 XYZ LB XYZ-1-002 14 GLUC Glucose CHEMISTRY 13.8 mmol/L PLASMA 4 VISIT4 15 XYZ LB XYZ-1-002 15 GLUC Glucose CHEMISTRY 14.1 mmol/L PLASMA 4 VISIT4 16 XYZ LB XYZ-1-002 16 GLUC Glucose CHEMISTRY 7.1 mmol/L PLASMA 4 VISIT4 17 XYZ LB XYZ-1-002 17 GLUC Glucose CHEMISTRY 5.4 mmol/L PLASMA 4 VISIT4 18 XYZ LB XYZ-1-002 18 GLUC Glucose CHEMISTRY 5.3 mmol/L PLASMA Y 4 VISIT4

Row LBDTC LBTPT LBTPTNUM

1 (cont) 2015-01-19T06:00 PRE-BREAKFAST 1 2 (cont) 2015-01-19T08:00 POST-BREAKFAST 2 3 (cont) 2015-01-19T11:00 PRE-LUNCH 3 4 (cont) 2015-01-19T13:00 POST-LUNCH 4 5 (cont) 2015-01-19T18:00 PRE-DINNER 5 6 (cont) 2015-01-19T19:00 POST-DINNER 6 7 (cont) 2015-01-19T21:00 BEDTIME 7 8 (cont) 2015-01-20T03:00 0:300 AM 8 9 (cont) 2015-01-20T06:00 PRE-BREAKFAST 9 10 (cont) 2015-02-16T06:00 PRE-BREAKFAST 1 11 (cont) 2015-02-16T08:00 POST-BREAKFAST 2 12 (cont) 2015-02-16T12:00 PRE-LUNCH 3 13 (cont) 2015-02-16T13:00 POST-LUNCH 4 14 (cont) 2015-02-16T18:00 PRE-DINNER 5 15 (cont) 2015-02-16T19:00 POST-DINNER 6 16 (cont) 2015-02-16T21:00 BEDTIME 7 17 (cont) 2015-02-17T03:00 0:300 AM 8 18 (cont) 2015-02-17T06:00 PRE-BREAKFAST 9



Parameter/time-point combinations were created for each time point within a visit. Rows 1-18: Show glucose measured in mmol/L as indicated by the parameter. The parameter code is set to GLUCSTD, and the variable BASETYPE (not

shown) will match the values of ATPT. Please note: BASETYPE is added for current conformance to the ADaM Implementation Guide. This use is currently being discussed as a topic for possible enhancement in a future ADaM Implementation Guide.

Rows 19-20: Show an example of glucose excursion from pre-breakfast to bedtime. Row 21-22: Show an example of a 24-hour glucose average. Table 5.1.2: ADSMBG Analysis Dataset Row USUBJID PARAM PARAMCD AVISIT TRTP ITTFL ABLFL BASE AVAL CHG ANL01FL ATPT LBSEQ

1 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 2 Drug A Y Y 6.8 6.8 Y Pre-Breakfast 1 2 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 2 Drug A Y Y 8.9 8.9 Y Post-Breakfast 2 3 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 2 Drug A Y Y 7.3 7.3 Y Pre-Lunch 3 4 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 2 Drug A Y Y 8.5 8.5 Y Post-Lunch 4 5 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 2 Drug A Y Y 7.9 7.9 Y Pre-Dinner 5 6 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 2 Drug A Y Y 8.1 8.1 Y Post-Dinner 6 7 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 2 Drug A Y Y 19.1 19.1 Y Bedtime 7 8 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 2 Drug A Y Y 6.4 6.4 Y 0:300 AM 8 9 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 2 Drug A Y Y 6.6 6.6 Y Pre-Breakfast Next day 9

10 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 4 Drug A Y 6.8 6.5 -0.3 Y Pre-Breakfast 10 11 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 4 Drug A Y 8.9 Post-Breakfast 11 12 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 4 Drug A Y 7.3 6.3 -1.0 Y Pre-Lunch 12 13 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 4 Drug A Y 8.5 9.1 0.6 Y Post-Lunch 13 14 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 4 Drug A Y 7.9 13.8 5.9 Y Pre-Dinner 14 15 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 4 Drug A Y 8.1 14.1 6.0 Y Post-Dinner 15 16 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 4 Drug A Y 19.1 7.1 -12.0 Y Bedtime 16 17 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 4 Drug A Y 6.4 5.3 -1.2 Y 0:300 AM 17 18 XYZ-1-002 Glucose (mmol/L) GLUCSTD Week 4 Drug A Y 6.6 6.3 -0.2 Y Pre-Breakfast Next Day 18

19 XYZ-1-002 Glucose Excursion Pre-Breakfast to Bedtime (mmol/L) GLUXFBM Week 2 Drug A Y Y 12.3 12.3 Y

20 XYZ-1-002 Glucose Excursion Pre-Breakfast to Bedtime (mmol/L) GLUXFBM Week 4 Drug A Y 12.3 0.6 -11.7 Y

21 XYZ-1-002 Glucose 24-Hour Average (mmol/L) GLUCAVM Week 2 Drug A Y Y 9.13 Y 22 XYZ-1-002 Glucose 24-Hour Average (mmol/L) GLUCAVM Week 4 Drug A Y 9.13 8.9 -.23 Y

Table 5.1.3: ADSMBG Dataset Metadata

Dataset Description Class Structure Purpose Keys Location Documentation ADSMBG SMBG Analysis

Data Basic Data Structure

One record per subject per parameter per visit

Analysis STUDYID, USUBJID, PARAMCD, ATPT, AVISIT

ADSMBG.xpt ADSMBG.SAS/SAP

Table 5.1.4: ADSMBG Variable Metadata


Format Codelist/Controlled


USUBJID Unique Subject Identifier text $20 ADSL.USUBJID PARAM Parameter text $60 See parameter-value metadata.






PARAMCD Parameter Code text $8 See parameter-value metadata. AVISIT Analysis Visit text $11 Week 2; Week 4; Week 8; Week 12;

Week 24, … Set to the corresponding visit from SDTM.

ATPT Analysis Timepoint text $20 See parameter-value metadata. STUDYID Study Identifier text $12 ADSL.STUDYID TRTP Planned Treatment text $15 Drug A; Placebo ADSL.TRT01P ITTFL Intent-To-Treat Population Flag text $1 Y; N ADSL.ITTFL AVAL Analysis Value float 8.1 See parameter-value metadata. BASE Baseline Value float 8.1 BASE = AVAL where ABLFL = “Y” for each corresponding

timepoint BASETYPE Baseline Type text $20 Pre-Breakfast; Post-Breakfast; Pre-

Lunch; Post-Lunch; Pre-Dinner; Post-Dinner; …

See parameter-value metadata.

CHG Change from Baseline float 8.1 Set to AVAL-BASE for each corresponding timepoint. ABLFL Baseline Record Flag text $1 Y Set to “Y” when AVISIT = “Baseline”. See SAP for visit

windowing. LBSEQ Sequence Number integer 4.0 LB.LBSEQ from the record in the SDTM LB domain

containing the result copied to AVAL. ANL01FL Analysis Record Flag 1 text $1 Y Set to “Y” on records intended for MMRM analysis, where

AVAL is not null. Table 5.1.5: ADSMBG Parameter [CL.PARAM.ADSMBG] Permitted Value (code) Glucose (mmol/L) Glucose Excursion Pre-Breakfast to Bedtime (mmol/L) Glucose 24-Hour Average (mmol/L)

Table 5.1.6: ADSMBG Parameter Code [CL.PARAMCD.ADSMBG] Permitted Value (Code) Display Value (Decode) GLUCSTD Glucose (mmol/L) GLUXFBM Glucose Excursion Pre-Breakfast to Bedtime (mmol/L) GLUCAVM Glucose 24-Hour Average (mmol/L)

Table 5.1.7: Parameter Value-Level List – ADSMBG [AVAL] Variable Where Type Length/Display



AVAL PARAMCD = “GLUCSTD”

float 8.1 Derived: AVAL = LB.LBSTRESN where LB.LBTESTCD =”GLUC” at the corresponding LBTPT to each ATPT

AVAL PARAMCD = “GLUXFBM”

float 8.1 Derived: Set AVAL to the difference between AVAL at Bedtime minus AVAL at Pre-Breakfast of the same day for PARAMCD = “GLUCSTD”.



Variable Where Type Length/Display Format

Codelist/Controlled Terms Source/Derivation/Comment

AVAL PARAMCD = “GLUCAVM”

float 8.1 Derived: Set AVAL to the average of glucose at all 9 timepoints during the 24 hour period from pre-breakfast to pre-breakfast next day

Table 5.1.8: Parameter Value-Level List – ADSMBG [ATPT] Variable Where Type Length/Display


ATPT PARAMCD = “GLUCSTD” text $23. Pre-Breakfast; Post-Breakfast; Pre-Lunch; Post-Lunch; Pre-Dinner; Post-Dinner Bedtime; 0:300 AM; Pre-Breakfast Next Day

Derived: Set to the corresponding value of SDTM LB.LBTPT

ATPT PARAMCD = “GLUXFBM” text $23. Derived: Set to blank ATPT PARAMCD = “GLUCAVM” text $23. Derived: Set to blank

Table 5.1.9: Parameter Value-Level List – ADSMBG [BASETYPE]

Variable Where Type Length/Display Format Codelist/Controlled Terms Source/Derivation/Comment

BASETYPE PARAMCD = “GLUCSTD” text $23. Pre-Breakfast; Post-Breakfast; Pre-Lunch; Post-Lunch; Pre-Dinner; Post-Dinner Bedtime; 0:300 AM; Pre-Breakfast Next Day

Derived: Set to match the ATPT

BASETYPE PARAMCD = “GLUXFBM” text $9 “EXCURSION” Derived: Set to “EXCURSION” BASETYPE PARAMCD = “GLUCAVM” text $7 “AVERAGE” Derived: Set to “AVERAGE”

5.2 Self-Monitored Glucose Analysis Results

5.2.1 Longitudinal Repeated Measures Model The table shell below represents a family of SMBG analyses where <Timepoint PARAM> could be any of:

• Pre-Breakfast Glucose (mmol/L) • 0:300 AM Glucose (mmol/L) • Post-Breakfast Glucose (mmol/L) • Pre-Breakfast-Bedtime Excursion (mmol/L) • Pre-Lunch Glucose (mmol/L) • 03:00 Hr-Next Day Pre-Breakfast Excursion (mmol/L) • Post-Lunch Glucose (mmol/L) • Bedtime-Next Day Pre-Breakfast Excursion (mmol/L) • Pre-Dinner Glucose (mmol/L) • Bedtime-0300 Hrs Excursion (mmol/L) • Post-Dinner Glucose (mmol/L) • Daily Glucose Average(mmol/L) • Bedtime Glucose (mmol/L)



Table 5.2.1: SMBG Timepoint Summary Table x.x Summary and Analysis of <Timepoint PARAM> Page x of y

Mixed Model Repeated Measures (MMRM)) hh:mm ddmmmyyyy <Analysis Population> <Study Name>

Visit Treatment N Mean (SD) (Min, Med, Max) LS Mean (SE)*a*b

B-A LSMean Diff (95% CI)*a*b

B/T Trt P-Val*a*b

Actual measurement of <Timepoint PARAM> Baseline Drug A xx xx.xx (x.xx) (xx.xx, xx.xx, xx.xx) xx.xx (x.xx) xx.xx(xx.xx, xx.xx) .xxx Drug B xx xx.xx (x.xx) (xx.xx, xx.xx, xx.xx) xx.xx (x.xx) Total xx xx.xx (x.xx) (xx.xx, xx.xx, xx.xx) ... ... ... ... ... ... ... ... Week 4 Drug A xx xx.xx (x.xx) (xx.xx, xx.xx, xx.xx) xx.xx (x.xx) xx.xx(xx.xx, xx.xx) .xxx Drug B xx xx.xx (x.xx) (xx.xx, xx.xx, xx.xx) xx.xx (x.xx) ... ... ... ... ... ... ... ... Week <n> Drug A xx xx.xx (x.xx) (xx.xx, xx.xx, xx.xx) xx.xx (x.xx) xx.xx(xx.xx, xx.xx) .xxx Drug B xx xx.xx (x.xx) (xx.xx, xx.xx, xx.xx) xx.xx (x.xx) ... ... ... ... ... ... ... ... Abbreviations: N = number of patients in <Analysis Population>; B/T = between; W/I = Within; CI = confidence interval; Diff = difference; LSMean = least squares mean; Max = maximum; Med = median; Min = minimum; N = total number of patients; p-Val = p-value; SD = standard deviation; SE = standard error; Trt = treatment. *a - MMRM model for post-baseline measures: [Response Variable = Baseline + Treatment + Visit + <covariates> + Treatment*Visit (Type III sums of squares)]. *b - ANOVA model for baseline measures: [Response Variable = Treatment (Type III sums of squares)]. Table 5.2.2: SMBG Longitudinal Repeated Measures Analysis Results Metadata Metadata Field Metadata DISPLAY IDENTIFIER Table x.x DISPLAY NAME Summary and Analysis of <Timepoint PARAM> - Mixed Model Repeated Measures (MMRM)) RESULT IDENTIFIER Pre-Breakfast Treatment difference results (LSMean, confidence interval, p-value) PARAM Glucose (mmol/L) PARAMCD GLUCSTD ANALYSIS VARIABLE AVAL ANALYSIS REASON Primary efficacy analysis as pre-specified in protocol ANALYSIS DATASET ADSMBG SELECTION CRITERIA ITTFL = “Y” and PARAMCD = “ GLUCSTD” and ATPT = “ Pre-Breakfast” and ANL01FL = “Y” DOCUMENTATION See SAP Section XX for details. Program: xxx.sas LS means and 95% CIs are based on repeated measures model adjusting for planned

treatment, baseline, Glucose value, visit, and visit*treatment interaction. PROGRAMMING STATEMENTS

PROC MIXED DATA = ADSMBG; WHERE ITTFL = “Y” and PARAMCD = “ GLUCSTD” and ATPT = “ Pre-Breakfast” and ANL01FL = “Y” CLASS TRTP AVISIT; MODEL AVAL = TRTP BASE AVISIT AVISIT*TRTP / DDFM = KR; LSMEANS TRTP / CL DIFF; RANDOM usubjid / subject = SUBJID TYPE = UN; RUN ;



5.2.2 Self-Monitored Glucose Plots The graph below shows a plot of 9-point self-monitored plasma glucose, which has been used instead of whole blood glucose. Results are shown in both conventional and SI units.

Figure 5.2.1: 9-Point Self-Monitored Plasma Glucose Profile at Baseline - Mean Plot - Full Analysis Set

Table 5.2.3: 9-Point SMPG Profile Analysis Results Metadata Metadata Field Metadata DISPLAY IDENTIFIER Figure 5.2.1 DISPLAY NAME 9-Point Self-Monitored Plasma Glucose Profile at Baseline - Mean Plot - Full Analysis Set RESULT IDENTIFIER Mean SMPG (mmol/L) PARAM Glucose (mmol/L) PARAMCD GLUCSTD ANALYSIS VARIABLE AVAL ANALYSIS REASON Pre-specified in protocol ANALYSIS DATASET ADSMBG SELECTION CRITERIA ITTFL = “Y” and PARAMCD = “ GLUCSTD” and ANL01FL = “Y” and AVISIT = “Baseline” DOCUMENTATION See SAP Section XX for details. Program: xxx.sas. PROGRAMMING STATEMENTS PROC MEANS DATA = ADSMBG; where <selection criteria>;

CLASS TRTP ATPT; VAR AVAL ; output out=means mean=mean stderr=stderr; RUN ; /** add points for upper and lower standard errors and plot **/

SMPG: Self monitored plasma glucose. Observed data. Error bars: ± Standard error (mean). The conversion factor between mmol/L and mg/dL is 0.0555.



5.3 Mixed-Meal Tolerance Test Dataset This example is based on a Phase III, parallel-group study designed to determine efficacy of Drug A for patients with Type II diabetes. The MMTT analysis will consist of an area-under-the-curve (AUC) analysis. The following tables provide examples of an LB excerpt from an SDTM dataset, a BDS structured dataset, analysis dataset metadata, and analysis-variable metadata for an exploratory mixed-meal tolerance test where AUC is calculated from baseline to endpoint. Note that some valuable variables may not be presented, as only selected variables were chosen to focus on the most important concepts. The example below is also based on the “Glucose” parameter, but can be applied to other MMTT-related laboratory values such as C-peptide, insulin levels, etc. Consider the following sample data from SDTM: Table 5.3.1: Glucose Excerpt from LB domain Row STUDYID DOMAIN USUBJID LBSEQ LBTESTCD LBTEST LBCAT LBSTRESN LBSTRESU LBSPEC LBBLFL LBFAST VISITNUM VISIT

1 XYZ LB XYZ-001-002 1 GLUC Glucose CHEMISTRY 4.8 mmol/L PLASMA Y Y 2 VISIT2 2 XYZ LB XYZ-001-002 2 GLUC Glucose CHEMISTRY 9.9 mmol/L PLASMA Y 2 VISIT2 3 XYZ LB XYZ-001-002 3 GLUC Glucose CHEMISTRY 9.3 mmol/L PLASMA Y 2 VISIT2 4 XYZ LB XYZ-001-002 4 GLUC Glucose CHEMISTRY 8.0 mmol/L PLASMA Y 2 VISIT2 5 XYZ LB XYZ-001-002 5 GLUC Glucose CHEMISTRY 5.9 mmol/L PLASMA Y 2 VISIT2 6 XYZ LB XYZ-001-002 9 GLUC Glucose CHEMISTRY 6.1 mmol/L PLASMA Y 4 VISIT4 7 XYZ LB XYZ-001-002 11 GLUC Glucose CHEMISTRY 8.7 mmol/L PLASMA 4 VISIT4 8 XYZ LB XYZ-001-002 12 GLUC Glucose CHEMISTRY 12.1 mmol/L PLASMA 4 VISIT4 9 XYZ LB XYZ-001-002 13 GLUC Glucose CHEMISTRY 9.8 mmol/L PLASMA 4 VISIT4

10 XYZ LB XYZ-001-002 14 GLUC Glucose CHEMISTRY 6.1 mmol/L PLASMA 4 VISIT4

Row LBDTC LBTPTNUM LBTPT 1(cont) 2015-01-19T08:00 1 0 MINUTE 2(cont) 2015-01-19T08:30 2 30 MINUTE 3(cont) 2015-01-19T09:00 3 60 MINUTE 4(cont) 2015-01-19T10:00 4 120 MINUTE 5(cont) 2015-01-19T11:00 5 180 MINUTE 6(cont) 2015-02-16T08:00 1 0 MINUTE 7(cont) 2015-02-16T08:30 2 30 MINUTE 8(cont) 2015-02-16T09:00 3 60 MINUTE 9(cont) 2015-02-16T10:00 4 120 MINUTE 10(cont) 2015-02-16T11:00 5 180 MINUTE

Parameters were created from each time point within a visit. Rows 1-10: Show glucose measured in mmol/L as spelled out in the parameter. The parameter code was set to GLUCSTD, and the variable BASETYPE (not

shown) will match the values of ATPT. Rows 11-16: Show examples of glucose excursion from fasting to 30, 60, and 120 minutes at baseline and at Week 4. Rows 17-19: Show an example of glucose AUC. Row 19: Show an example of a derived Last Observation Carried Forward (LOCF) corresponding to an AVISIT of Endpoint for baseline to endpoint

analysis.



Rows 20-21: Show an example of glucose incremental AUC. Table 5.3.2: ADMMTT Analysis Dataset Row USUBJID PARAM PARAMCD ATPT AVISIT TRTP ANL01FL ITTFL AVAL LBSEQ DTYPE

1 XYZ-001-002 Glucose (mmol/L) GLUCSTD 0 Min Baseline Drug A Y Y 4.8 1 2 XYZ-001-002 Glucose (mmol/L) GLUCSTD 30 Min Baseline Drug A Y Y 9.9 2 3 XYZ-001-002 Glucose (mmol/L) GLUCSTD 60 Min Baseline Drug A Y Y 9.3 3 4 XYZ-001-002 Glucose (mmol/L) GLUCSTD 120 Min Baseline Drug A Y Y 8.0 4 5 XYZ-001-002 Glucose (mmol/L) GLUCSTD 180 Min Baseline Drug A Y Y 5.9 5 6 XYZ-001-002 Glucose (mmol/L) GLUCSTD 0 Min Week 4 Drug A Y Y 6.1 6 7 XYZ-001-002 Glucose (mmol/L) GLUCSTD 30 Min Week 4 Drug A Y Y 8.7 7 8 XYZ-001-002 Glucose (mmol/L) GLUCSTD 60 Min Week 4 Drug A Y Y 12.1 8 9 XYZ-001-002 Glucose (mmol/L) GLUCSTD 120 Min Week 4 Drug A Y Y 9.8 9

10 XYZ-001-002 Glucose (mmol/L) GLUCSTD 180 Min Week 4 Drug A Y Y 6.1 10 11 XYZ-001-002 Glucose Excursion 0 - 30 Min (mmol/L) GLUX030 Baseline Drug A Y Y 5.1 12 XYZ-001-002 Glucose Excursion 0 - 60 Min (mmol/L) GLUX060 Baseline Drug A Y Y 4.5 13 XYZ-001-002 Glucose Excursion 0 -120 Min (mmol/L) GLUX0120 Baseline Drug A Y Y 3.2 14 XYZ-001-002 Glucose Excursion 0 - 30 Min (mmol/L) GLUX030 Week 4 Drug A Y Y 2.6 15 XYZ-001-002 Glucose Excursion 0 - 60 Min (mmol/L) GLUX060 Week 4 Drug A Y Y 6.0 16 XYZ-001-002 Glucose Excursion 0 -120 Min (mmol/L) GLUX0120 Week 4 Drug A Y Y 3.7 17 XYZ-001-002 Glucose Area Under Curve GLUCAUC Baseline Drug A Y Y 24.075 18 XYZ-001-002 Glucose Area Under Curve GLUCAUC Week 4 Drug A Y Y 27.800 19 XYZ-001-002 Glucose Area Under Curve GLUCAUC Endpoint Drug A Y Y 27.800 LOCF 20 XYZ-001-002 Glucose Incremental AUC GLUCIAUC Baseline Drug A Y Y 9.675 21 XYZ-001-002 Glucose Incremental AUC GLUCIAUC Week 4 Drug A Y Y 9.500

Table 5.3.3: ADMMTT Analysis Dataset Metadata

Dataset Description Class Structure Purpose Keys Location Documentation ADMMTT Mixed-Meal Tolerance

Test Analysis Data Basic Data Structure

One record per subject per parameter per visit per timepoint

Analysis STUDYID, USUBJID, PARAMCD, ATPT, AVISIT

ADMMTT.xpt ADMMTT.SAS/SAP

Table 5.3.4: ADMMTT Analysis-Variable Metadata



USUBJID Unique Subject Identifier

text $20 ADSL.USUBJID

PARAM Parameter text $36 See parameter-value metadata. PARAMCD Parameter Code text $8 See parameter-value metadata. ATPT Analysis Time

Point text $40 See parameter-value metadata.

AVISIT Analysis Visit text $11 Baseline; Week 2; Week 4; Week 8; Week 12; Week 24; Endpoint

Refer to the SAP for windowing. Endpoint visit is imputed using LOCF algorithm. See parameter-value metadata.

STUDYID Study Identifier text $12 ADSL.STUDYID TRTP Planned Treatment text $15 ADSL.TRT01P



Other parameters of interest, depending on the analyses required for the particular compound could be: insulin, pro-insulin, and/or C-peptide. Each of them could have derived parameters such as: excursion, AUC, incremental AUC, etc. Table 5.3.5: ADMMTT Parameter [CL.PARAM.ADMMTT] Permitted Value (code) Glucose (mmol/L) Glucose Excursion 0 - 30 Min (mmol/L) Glucose Excursion 0 - 60 Min (mmol/L) Glucose Excursion 0 -120 Min (mmol/L) Glucose Area Under Curve Glucose Incremental AUC

Table 5.3.6: ADMMTT Parameter Code [CL.PARAMCD.ADMMTT] Permitted Value (Code) Display Value (Decode) GLUCSTD Glucose (mmol/L) GLUX030 Glucose Excursion 0 - 30 Min (mmol/L) GLUX060 Glucose Excursion 0 - 60 Min (mmol/L) GLUX0120 Glucose Excursion 0 -120 Min (mmol/L) GLUCAUC Glucose Area Under Curve GLUCIAUC Glucose Incremental AUC

Table 5.3.7: Parameter Value-Level List – ADMMTT [AVAL]




AVAL PARAMCD = “GLUCSTD” float best12. Derived: AVAL = LB.LBSTRESN where LB.LBTESTCD = “GLUC” at the corresponding LBTPT to each ATPT

ITTFL Intent-to-Treat Population Flag

text $1 Y; N ADSL.ITTFL

AVAL Analysis Value float 8.1 See parameter-value metadata. LBSEQ Sequence Number integer 4.0 LB.LBSEQ from the record in the SDTM LB domain containing AVAL. DTYPE Derivation Type

text 10 See parameter-value metadata.

ANL01FL Analysis Flag 1 text $1 Y Set to “Y” on records intended for MMRM analysis, where AVAL is not null. BASE Baseline Value float 8.1 BASE = AVAL where ABLFL = “Y” for each corresponding timepoint BASETYPE Baseline Type text $20 Pre-Breakfast; Post-Breakfast;

Pre-Lunch; Post-Lunch; Pre-Dinner; Post-Dinner, …

See parameter-value metadata.

ABLFL Baseline Record Flag

text $1 Y Set to “Y” when AVISIT = “Baseline”. See SAP for visit windowing.

CHG Change From Baseline

float 8.1 AVAL-BASE






AVAL PARAMCD = “GLUX030” float best12. Derived: Set to the difference between Glucose at time 30 min minus Glucose value at 0 minutes AVAL PARAMCD = “GLUX060” float best12. Derived: Set to the difference between Glucose at time 60 min minus Glucose value at 0 minutes AVAL PARAMCD = “GLUX0120” float best12. Derived: Set to the difference between Glucose at time 120 min minus Glucose value at 0 minutes AVAL PARAMCD = “GLUCAUC” float best12. Derived: Set AVAL = the sum from i = 1 to n - 1 of ((Ti+1 - Ti) * (Ci + Ci+1))/2 where

n = the number of timepoints Ti = the timepoint in hours at time i Ti+1 = the timepoint in hours at time i + 1 Ci = LB result at time i Ci+1 = LB result at time i + 1

AVAL PARAMCD = “GLUCIAUC” float best12. Derived: Set AVAL = the sum from i = 1 to n - 1 of ((Ti+1 - Ti) * (Ei + Ei+1))/2 where n = the number of timepoints Ti = the timepoint in hours at time i Ti+1 = the timepoint in hours at time i + 1 Ei = Glucose excursion at time i Ei+1 = the Glucose excursion at time i + 1

Table 5.3.8: Parameter Value Level List – ADMMTT [AVISIT] Variable Where Type Length/Display



AVISIT PARAMCD in (“GLUCSTD” “GLUX030”, “GLUX060”, “GLUX0120”, “GLUCIAUC”)

text $10. Baseline; Week 2; Week 4; Week 8; Week 12; Week 24

Derived: AVISIT mapped from SDTM VISIT when possible. See SAP for windowing rules.

AVISIT PARAMCD = “GLUCAUC” text $10. Baseline; Week 2; Week 4; Week 8; Week 12; Week 24; Endpoint

Derived: Endpoint visit is added using LOCF

Table 5.3.9: Parameter Value-Level List – ADMMTT [DTYPE] Variable Where Type Length/Display



DTYPE PARAMCD in (“GLUCSTD” “GLUX030”, “GLUX060”, “GLUX0120”, “GLUCIAUC”)

text $10. Derived: Set to blank

DTYPE PARAMCD = “GLUCAUC” text $10. LOCF Derived: Set to LOCF for the additional Endpoint visit Table 5.3.10: Parameter Value-Level List – ADMMTT [ATPT] Variable Where Type Length/Display



ATPT PARAMCD = “GLUCSTD” text $10. 0 Min; 30 Min; 60 Min; 120 Min; 180 Min

Derived: Set to the corresponding ADaM version of SDTM LB.LBTPT

ATPT PARAMCD in ( “GLUX030”, “GLUX060”, “GLUX0120”, “GLUCAUC”, “GLUCIAUC”)

text $10. Derived: Set to blank



Table 5.3.11: Parameter Value-Level List – ADMMTT [BASETYPE]

Variable Where Type Length/Display Format Codelist/Controlled Terms Source/Derivation/Comment

BASETYPE PARAMCD = “GLUCSTD” text $10. 0 Min; 30 Min; 60 Min; 120 Min; 180 Min

Derived: Set to match the ATPT.

BASETYPE PARAMCD in (“GLUX030”, “GLUX060”, “GLUX0120”)

text $10. EXCURSION Derived: Set to “EXCURSION”

BASETYPE PARAMCD in (“GLUCAUC”, “GLUCIAUC”)

text $10. AUC Derived: Set to “AUC”

5.4 Mixed Meal Tolerance Test Analysis Results The following graph shows an example of glucose levels at baseline to endpoint for one patient. The shaded area describes the glucose AUC change from baseline to endpoint for the same patient.

Figure 5.4.1: Change of Glucose AUC from Baseline to Endpoint

Once the AUC value has been calculated for each patient, common analyses of AUC include ANCOVA on actual values and change from baseline. Below is an example of an analysis with change from baseline included.

Glucose [mmol/L]

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

Time Point (min)

0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240

Change of Glucose AUC from Baseline to Endpoint

Baseline Endpoint



Table 5.4.1: MMT Area Under the Curve Glucose Table Shell Summary and Analysis of AUC Glucose (0-xyz minutes) Page x of y

ANCOVA at X Weeks hh:mm ddmmmyyyy <Analysis population> Study ABCDEF

Variable analyzed: AUC Glucose (mmol*hr/L) Actual value Change from Baseline Visit (Week) Treatment N Mean SD Min Median Max Mean SD Min Median Max LSM SE

p-value*a

Baseline Drug C xx xx.xx xx.xx xx.xx xx.xx xx.xx Drug A xx xx.xx xx.xx xx.xx xx.xx xx.xx Drug B xx xx.xx xx.xx xx.xx xx.xx xx.xx x (yy) Drug C xx xx.xx xx.xx xx.xx xx.xx xx.xx -x.xx x.xx -xx.xx -x.xx xx.xx -x.xx x.xx <.xxx Drug A xx xx.xx xx.xx xx.xx xx.xx xx.xx -x.xx x.xx -xx.xx -x.xx xx.xx -x.xx x.xx <.xxx Drug B xx xx.xx xx.xx xx.xx xx.xx xx.xx -x.xx x.xx -xx.xx -x.xx xx.xx -x.xx x.xx <.xxx pairwise p-value, 95% CI*b vs Drug C vs Drug A Drug A .xxx, (-x.xx, x.xx) Drug B .xxx, (-x.xx, x.xx) .xxx, (-x.xx, x.xx) ANCOVA: analysis of covariance, AUC: Area under the curve, CI: confidence interval, hrs: hours, hr: hour, LSM: least-squares mean, Max = maximum, Min: minimum, mmol/L: millimole per liter, N: total number of patients with non-missing value at baseline and specified visit in specified treatment arm, OAM: oral antihyperglycemic medication, SD: standard deviation, SE: standard error, vs: versus. *a - Within group p-values are from t-tests on LS Mean change from baseline. *b - Treatment pairwise comparison p-value and 95% CI of pairwise difference of LS Means of change from baseline are from Analysis of Covariance (ANCOVA) Model: Change from Baseline = Baseline + Pooled Country for Test Meal + Prior Medication Group(previous OAM vs. no previous OAM) + Treatment (Type III sum of squares)

The Analysis results metadata for the observed AUC analysis are shown below. Table 5.4.2: MMTT Area Under the Curve Analysis Results Metadata Metadata Field Metadata DISPLAY IDENTIFIER Table 5.4.1 DISPLAY NAME Summary and Analysis of Glucose AUC - ANCOVA RESULT IDENTIFIER Treatment difference results (LSMean, confidence interval, p-value) PARAM Glucose Area Under the Curve PARAMCD GLUCAUC ANALYSIS VARIABLE AVAL ANALYSIS REASON Primary efficacy analysis as pre-specified in protocol ANALYSIS DATASET ADMMTT SELECTION CRITERIA ITTFL = “Y” and PARAMCD =”GLUCAUC” and ANL01FL = “Y” DOCUMENTATION See SAP Section XX for details. Program: xxx.sas PROGRAMMING STATEMENTS

Appendices Appendix A: CFAST Diabetes ADaM Sub-Team

Name Institution/Organization Rachael Zirkle, Team Leader Eli Lilly and Company Susan Kenny Maximum Likelihood Nate Friemark The Griesser Group Birgitte Ronn Novo Nordisk Paula Martin Independent Consultant Alan Zimmerman Eli Lilly and Company Karina Stender Novo Nordisk Stephen Faulkner Pfizer Isaac Swanson Eli Lilly and Company Mario Widel Eli Lilly and Company Yingshan You Johnson & Johnson Jennie G. Jacobson Eli Lilly and Company

Appendix B: References 1. Seaquist ER, Anderson J, Childs B, et al. Hypoglycemia and diabetes: a report of a workgroup of the american

diabetes association and the endocrine society. Diabetes Care. 2013;36(5):1384-95. doi: 10.2337/dc12-2480. 2. Bulsara MK, Holman CD, Davis EA, Jones TW. Evaluating risk factors associated with severe hypoglycaemia

in epidemiology studies-what method should we use? Diabet Med. 2004;21(8):914-9. doi: 10.1111/j.1464-5491.2004.01250.x.

3. Aschner P, Chan J, Owens DR, et al. Insulin glargine versus sitagliptin in insulin-naive patients with type 2 diabetes mellitus uncontrolled on metformin (EASIE): a multicentre, randomised open-label trial. Lancet. 2012;379(9833):2262-9. doi: 10.1016/S0140-6736(12)60439-5.



Appendix C: Representations and Warranties, Limitations of Liability, and Disclaimers

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