statistical modeling and graphical analysis of safety data in ... 2007...gastritis...

Copyright ©1999-2007Insightful Corporation. All Rights Reserved.

Statistical Modeling and Graphical Analysis of Safety Data in Clinical Trials

Michael O’Connell

November, 2007

www.splus.mathsoft.com 2Copyright ©1999-2007Insightful Corporation. All Rights Reserved. 2

1. Technical and Business Challenges with Safety Data+ Valid Inference; Type I and Type II Error+ Market-wide hot button issue

2. Statistical and Graphical Analysis of Safety Data+ Adverse Events+ Lab Measurements

3. Review and Reporting + Interactive Clinical / Safety Review and Reporting

4. Safety Analysis to the Future

Outline


Karl Peace, Georgia Southern + talking points

Harry Southworth, Astra-Zeneca + brlr and imsev models / software packages

Ohad Amit, Peter Lane, Susan Duke, GSK + Graphical collaboration

Amy Xia, Kefei Zhou, Haijun Ma, Amgen + Graphical collaboration

Dawn Woodard, Insightful, Duke ISDS + Bayes software packages

Acknowledgements

Much emphasis on design and analysis methods for efficacy

Safety data are collected as concomitant informationSafety data has not been a focus for statistical / graphical analysis /

methodology development

Safety data are typically reported as tables and listingsDifficult to review and interpret

Current Issues with Safety Data Analysis


Monitors

Data Mgt

Clinical

Statistics

Statistics

Programming

Publishing

Medical Writing

Clinical Trial Environment – Use Cases, Actors

Instream Unblinded

Statistics

Clinical

Management

ProtocolSAP

DataCleaning

Safety InstreamClinicalReview

CSRNDA

Labeling

JournalsScientific Meetings

Trial Design EDA / Review Report: Submission, Publication

Design


Business Issues in Pharmaceutical Drug Development

Drug Safety

Avandia (GSK diabetes drug)+ Metadata analysis by academic - 43% increase in heart attack+ Analysis / presentation flawed

• Risk “increased” from 5/1000 to 7/1000

Vioxx (Merck pain drug)+ Post-marketing analysis - some increase in heart attack, stroke+ Merck pulled Vioxx+ 16 v 4 MI’s in Merck Phase IV trial

+ Thought they were facing class effect

GSK Stock: Avandia Effect

Merck Stock: Vioxx Effect


Adverse EventsWhich adverse events are elevated in treatment vs. placebo?

Patterns of AE onset in treatment vs. placebo?

Treatment effects and patterns - population level analysis

LabsWhich patients have abrupt changes in lab tests? Is there temporal causality of drug intake?

Are there subjects with elevation on multiple labs?Patient level analysis

Other relevant dataCon meds, demographics, vitals, exposure

Patient level profiling

Safety Questions and Data Sources


Some data issues to address+ Many variables (labs, AE’s)+ Sparse data – law of three

Need to worry about both Type I and Type II error

Analytic approaches+ Assess treatment effect directly / post-hoc testing multiplicities + Borrow strength – Bayes methods - power to detect true AE elevation+ Analyze data together at subject/visit level – inside-out machine

learning

Graphics approaches+ Targeted statistical graphics: AE’s, labs at population and patient

level+ Interactive data review: population-to-patient level



Phase 2 NeuroPsych Therapeutic Area: ‘Prostinol’+ Establish Dose-Response+ Document Safety Profile+ 6 months, ~ 210 pts, placebo and 2 doses

Patient-level data+ AEs+ Labs+ etc.

Clinical Trial Example


ADVERSE EVENTS

Adverse Event Analysis

Adverse Events Questions Which adverse events are elevated in treatment vs. placebo?

Patterns of AE onset in treatment vs. placebo?

Treatment effects and patterns - population level analysis

SOMNOLENCEOEDEMA PERIPHERAL

NASAL CONGESTIONATRIOVENTRICULAR BLO

ATRIAL FIBRILLATIONMYOCARDIAL INFARCTIO

SYNCOPEELECTROCARDIOGRAM ST

HEADACHESALIVARY HYPERSECRET

COUGHSKIN IRRITATION

FATIGUEHYPERHIDROSIS

SINUS BRADYCARDIAUPPER RESPIRATORY TR

NASOPHARYNGITISVOMITING

RASHDIZZINESS

NAUSEAPRURITUS

DIARRHOEAABDOMINAL PAIN

0 10 20 30

Phase 2 ProstinolAE PT: Prostinol High Dose v Placebo

data: C:\Program Files\Insightful\splus80\users\flexB\aeAllout: C:\Program Files\Insightful\splus80\users\flexB\outputs

Percent (%)

PlaceboProstHigh

AE PT Risk: Dot Plot

Adverse Event Double Dot PlotPlacebo N=72ProstHigh N=71

0 5 10 15 20 25 30 35

SALIVARY HYPERSECRETATRIOVENTRICULAR BLOUPPER RESPIRATORY TR

NASAL CONGESTIONATRIAL FIBRILLATION

OEDEMA PERIPHERALELECTROCARDIOGRAM ST

NASOPHARYNGITISHEADACHE

MYOCARDIAL INFARCTIOCOUGH

FATIGUEHYPERHIDROSIS

SINUS BRADYCARDIASYNCOPE

RASHNAUSEA

SKIN IRRITATIONSOMNOLENCE

VOMITINGDIZZINESS

DIARRHOEAPRURITUS

ABDOMINAL PAIN

Term vs pct

1 2 3 4 5 6

Term vs rrEst

Term

pct rrEst

Relative Risk and interval from Bayes B&B model

AE PT Risk: Double Dot Plot


“Safety assessment is one area where frequentiststrategies have been less applicable. Perhaps Bayesian approaches in this area have more promise.”

George Chi, H.M. James Hung, Robert O’Neill (FDA CDER), Pharmaceutical Report, 2002.

Bayesian Methods for Safety Data


Minimizing false negatives and false positives

AE data are sparse – can miss a true positive+ For an AE that occurs 1/100 in the population at large, you need 300

subjects to see just one occurrence of that AE with 95% confidence

+ Bayes methods borrow strength (shrinkage) across the data (eg across body systems) to keep up the power to detect a true AE elevation

There are many AE’s and labs to evaluate – false positives?+ Usual multiplicity argument doesn’t apply… adjust MI for fatigue !?

+ Bayes models can be parameterized to address treatment-emergence for individual AE’s

Bayesian Methods for Safety Data

SkeletalMuscularLymphaticEndocrineDigestiveNervousCardiovascularMale ReproductiveFemale ReproductiveUrinary

Borrowing Strength with Body Systems

SkeletalMuscularLymphaticEndocrineDigestiveNervousCardiovascularMale ReproductiveFemale ReproductiveUrinary

NauseaVomitingAnorexiaCandidiasisConstipationDiarrheaGastroenteritis…

Borrowing Strength with Body Systems

B body systems

ki adverse effects within body system i

For AEij, i = 1, . . ., B, j = 1, . . .,ki

Control: xij events in nC patientsTreatment: yij events in nT patients

H0: cij = tij, where cij & tij are event rates

logit(cij) = γij

logit(tij) = γij + θij

θij are log odds ratios

θij = 0 => Pr(subject has AEij) is same for trt and ctl

γij ~ N(μγi, σγ2)

Model based on Berry and Berry, 2004

Hierarchical Models for AE Counts

θij ~ πi I{0} + (1–πi)N(μθi, σθi2)

πi is probability that the treatment has no effect on an AE in body system i

πi ~ Beta(aπ, bπ), i = 1, …, B Priors on aπ, bπ are chosen to be symmetric

=> Prior Pr(θij= 0) = prior Pr(no trt effect on AEij) = 0.5

=> Addresses multiple comparisons issue directly

μγi, σγ2 μθi, σθi

2 πi are same for PTs within SOCs

=> Borrow strength within SOCs

μγi, μθi, πi are modeled as random effects

=> Borrow strength across SOCs

Model based on Berry and Berry, 2004

Hierarchical Models for AE Counts


1. Read in and preprocess the data

2. Specify the model

3. Specify parameters for which posterior samples are desired

4. (optional) Specify the initial values for the MCMC

5. Fit the model+ Obtain the samples and return an object of class posterior

6. Run convergence diagnostics on the posterior object

7. Use the posterior samples for parameter inference + Summarize the model results graphically

8. Deploy as part of interactive clinical review application

Bayes modeling - steps

Lag

Aut

ocor

rela

tion

0 50 100 150 200

-1.0

0.0

1.0

c[3,1], Chain 1

Lag

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0 50 100 150 200

-1.0

0.0

1.0

a[3,1], Chain 1

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0 50 100 150 200

-1.0

0.0

1.0

a[5,3], Chain 1

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-1.0

0.0

1.0

c[5,3], Chain 1

Lag

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0 50 100 150 200

-1.0

0.0

1.0

mu.c[3], Chain 1

Lag

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tion

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-1.0

0.0

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mu.a[3], Chain 1

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tion

0 50 100 150 200

-1.0

0.0

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s.rho.a, Chain 1

Lag

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0 50 100 150 200

-1.0

0.0

1.0

s.rho.c, Chain 1

Lag

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0 50 100 150 200

-1.0

0.0

1.0

s.tau.c, Chain 1

Iterations

0 10000 20000 30000 40000

0.02

0.04

0.06

0.08

0.10

Trace of theta.t[3,1]

Model diagnostics

Autocorrelation Plot

Trace Plot: theta_treat

STUPORLETHARGY

HEMIANOPIA HOMONYMOUSSKIN EXFOLIATION

RASHDIARRHOEA

HYPERHIDROSISNAUSEA

URTICARIAABDOMINAL PAIN

PRURITUS GENERALISEDSINUS BRADYCARDIA

PRURITUSSKIN IRRITATION

BALANCE DISORDERTRANSIENT ISCHAEMIC ATTACK

VOMITINGSOMNOLENCE

SYNCOPEDIZZINESS

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0

||||

|||||

||

||||

||

|||

log-10 Empirical RR Bayes Posterior Mean of Log-10 Relative Risk with 99% Credible Interval

AE PT Relative Risk: Bayes Interval Plot

AE PT Relative Risk: Bayes p-Risk Plot (interactive)

Figure 5.1.2. Kaplan Meier Plot on Time to Cholecystitis by Therapy TypeSafety Analysis Set

Monotherapy Combotherapy

Even

t-fre

e Pr

obab

ility

0.0

0.80

0.85

0.90

0.95

1.00

Time on Study (Month)

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32

Subjects at risk:

CombotherapyMonotherapy

85339

77325

66304

59256

48201

42158

26136

14103

884

369

060

052

036

028

024

017

010

At Risk Events 12-Month Estimate

Monotherapy 339 10 3%Combotherapy 85 6 9%

AE Onset

Different study - combination therapy

-log(

P-v

alue

)

-4 -2 0 2 4

01

23

Rash

Migraine Abdominal pain

Gastritis

Fall

-log(

P-v

alue

)

-6 -4 -2 0 2 4

01

23

Rash

Migraine Abdominal pa

Gastritis

Fall

KM Incidence Difference (%): Treatment - Placebo, at 12 mon and 24 mon

P-risk Plot (incidence difference)

AE Onset: log-rank P-value v KM Incidence Difference (combination therapy study)


Trtij = f (PTj) + eiji = 1, …, 213 subjects

j = 1, …, 199 AEs

Consider f as recursive partitioning, random forests etc. e.g.

library(arbor)

arb.rfAE <- arbor(treatment ~ . , model=T, data = rfAE)

plot.arbor(arb.rfAE)

text(arb.rfAE, use.n=T, all=T)

summary.arbor(arb.rfAE)

Treatment Emergence: Inside-out machine learning

|ABDOMINAL.PAIN>=0.5PRURITUS>=0.5

SINUS.BRADYCARDIA>=0.5

DIZZINESS>=0.5

RASH>=0.5

Treatment140/72Treatment

53/8Treatment

87/64

Treatment33/7

Control 54/57

Treatment7/0

Control 47/57

Treatment6/1

Control 41/56

Treatment5/2

Control 36/54

Treatment Emergence: Inside-out Tree

EYE.ALLERGYTRANSIENT.ISCHAE

HEADACHESKIN.IRRITATION

EAR.PAINCONSTIPATION

NAUSEAELECTROCARDIOGRA

CHILLSCATARACT.OPERATI

DRUG.ERUPTIONATRIOVENTRICULAR

BACK.PAINRASH.PRURITIC

ATRIAL.HYPERTROPANXIETY

DIARRHOEAUPPER.RESPIRATOR

RASHSINUS.BRADYCARDI

SYNCOPEDIZZINESSPRURITUS

ABDOMINAL.PAIN

2 4 6 8

Phase 2 ProstinolAE PT: Prostinol High Dose v Placebo

Inside Out Bagged Tree Model

Code: C:\Program Files\Insightful\splus80\users\flexB\driverIORandForest.sscOutput: C:\Program Files\Insightful\splus80\users\flexB\outputs\InsideOutBaggedTree

Variable Importance

Treatment Emergence: Inside-out Bagged Tree


AEs as response: sparse data enabled, includes demographics as explanatory variables

+ Removes O(n-1) bias from likelihood estimator through score function correction

+ Penalty on likelihood (Jeffrey’s prior if canonical param of exp family) stabilizes computation, shrinks parameter estimates

+ Needed for sparse data + Simple to implement: coxph with ridge, brlr package for 0/1 data

+ Use change in penalized deviance as evidence of treatment effect

Full model: brlr (PTj ~ trt + ns(age) + sex + race)

Reduced model: brlr (PTj ~ ns(age) + sex + race)

Firth (1993)

Treatment Emergence: Penalized Cox, logistic regression

AE.EPISTAXISAE.ATRIAL.FIBRILLATION

AE.PYREXIAAE.COUGH

AE.SHOULDER.PAINAE.AGITATION

AE.SOMNOLENCEAE.SYNCOPE

AE.HEADACHEAE.ANXIETY

AE.SKIN.IRRITATIONAE.SALIVARY.HYPERSECRETION

AE.FATIGUEAE.HYPERHIDROSIS

AE.UPPER.RESPIRATORY.TRACT.INFECAE.NAUSEA

AE.NASOPHARYNGITISAE.RASH

AE.VOMITINGAE.DIARRHOEA

AE.SINUS.BRADYCARDIAAE.DIZZINESSAE.PRURITUS

AE.ABDOMINAL.PAIN

5 10 15 20

AE Preferred Term Treatment Effects

Change in (Penalized) Deviance

AE Treatment Emergence: Penalized logistic regression

Penalized logistic regression: dizziness

RACECaucasianBlack Other

SEXMale Female

AGE

LOW DOSE HIGH DOSE PLACEBO

FDA: Application for Approval to Market New Drug


Treatment comparison of AE counts/proportions+ Bayes mixture model + Aggregate AE PT by treatment (with SOC or SMQ borrowing)

Treatment emergence analysis at patient-level + Inside out machine learning + Arbor and Bagging (other ensembles also work)

AE time to onset analysis+ Kaplan Meier, p-Risk plots (log rank p-value v incidence difference)

Bias-reduced logistic regression – treatment + covariates+ Natural inclusion of covariables (sex, age, race etc.)

Graphics + Treatment effect: Dot plot, double dot plot, interval plot, p-Risk plot+ Onset: Kaplan Meier, p-Risk plot

We really can do a lot better than paper review of line listings !!!!!

AE Analysis – Summary


LABORATORY MEASUREMENTS

Lab Measurements

LabsWhich patients have abrupt changes in lab tests? Is there temporal causality of drug intake?

Are there subjects with elevation on multiple labs?Patient level analysis


“Hy’s Law” was developed by Hyman Zimmerman as criteria for evaluating drug induced liver injury

Potential for severe drug-related hepatotoxicity signaled by three components:

+ The drug causes hepatocellular injury, shown by more frequent 3-fold or greater elevations above upper limit of normal (ULN) of ALT or AST than the control agent.

+ Among subjects showing such AT elevations, often with ATs much greater than 3xULN, some cases also show elevation of serum TBL to 2xULN, without initial findings of cholestasis (manifested by a substantial increase in serum alkaline phosphatase activity (ALP)).

+ No other reason can be found to explain the combination of increased AT and TBL, such as viral hepatitis A, B, or C, preexisting or acute liver disease, or another drug capable of causing the observed injury.

“Hy’s Law” can be evaluated as part of the scatter matrix by generating different symbols for subjects who meet the criteria

Hy’s Law

Review Graphic: Labs Hy’s Law Plot: AST & Bilirubin

10^-0.5

1

3

10

32

10^-0.5 1 3 10 32 10^-0.5 1 3 10 32 10^-0.5 1 3 10 32

10^-0.5

1

3

10

32

10^-0.5

1

3

10

32

ALKP (xULN) ALT (xULN) AST (xULN)

Tota

l.Bili

. (xU

LN)

AS

T (x

ULN

)A

LT (x

ULN

)

Review Graphic: Labs Hy’s Law Plot: Hypothetical Data


Graphic considerations - what pairs of points to plot?+ Maximum of each LFT+ Maximum ALT value with all corresponding LFTs+ Maximum Bilirubin value with all corresponding LFTs

Questions from these plots+ Do ALT and AST track together?+ Are there simultaneous elevations in ALT/AST and Bilirubin?+ What is the time-course of the elevations?+ Can patients be re-challenged?

Hy’s Law Plot: Considerations

Review Graphic: Labs Temporal/Treatment Association/Causality: Profile Plot

Review Graphic: Labs New Guidance for DILI: embodies Hy’s Law


FDA Guidance suggests 3 indicators for DILI+ An excess of AT elevations to >3xULN compared to a control group

+ Marked elevations of AT to 5x-, 10x-, or 20xULN in smaller numbers of subjects in the test drug group and not seen (or seenmuch less frequently) in the control group

+ One or more cases of elevated bilirubin to >2xULN in a setting of pure hepatocellular injury (no evidence of obstruction, such as elevated ALP in gall bladder or bile duct disease, malignancy), with no other explanation (viral hepatitis, alcoholic or autoimmune hepatitis, other hepatotoxic drugs), accompanied by an overall increased rate of AT elevations >3xULN in the test drug group compared to placebo

New Guidance for DILI: embodies Hy’s Law


FDA Guidance suggests stopping treatment for DILI if+ ALT or AST >8xULN

+ ALT or AST >5xULN for more than 2 weeks

+ ALT or AST >3xULN and (TBL >2xULN or INR >1.5)

+ ALT or AST >3xULN with the appearance of worsening of fatigue, nausea, vomiting, right upper quadrant pain or tenderness, fever, rash, or eosinophilia

New Guidance for DILI: embodies Hy’s Law

Review Graphic: Labs Temporal/Treatment Association/Causality: Shift Plot

Review Graphic: Labs Shift Plot for other Labs: Renal

Shift Plots for other Labs - Interactive

Review Graphic: Labs Patient Profile Plot for Other Labs: Electrolytes

Note: Toxicities can include elevations or depressions: don’t just use /ULN


Trtij = f (labj) + eiji = 1, …, 29 labs ( 8 wk value / baseline )

j = 1, …, 213 subjects ( 154 with baseline and 8 weeks )

Consider f as bagging, forests etc.

library(forests)

labs.forest.out <- forest( TRT ~ ., data = labs.in4,

classVote = T,

nTrees = pr.nTrees,

boost = F,

treeMethod = "class",

nRandomSplitVars = pr.nRandomSplitVars,

control= rfEachTreeControl(minsplit=4, minbucket=2))

Inside out machine learning (arbor / forest)

CALCIUMHEMOGLOBIN

SODIUMCREATININE

ERYTHROCYTESERY..MEAN.CORPUSCULAR.HEMO

LYMPHOCYTESALKALINE.PHOSPHATASE

HEMATOCRITGLUCOSE

MONOCYTESPLATELET

CREATINE.KINASECHOLESTEROL

ERY..MEAN.CORPUSCULAR.HB.CUREA.NITROGEN

ASPARTATE.AMINOTRANSFERASEPHOSPHATE

BILIRUBINGAMMA.GLUTAMYL.TRANSFERASE

ALBUMINLEUKOCYTES

ALANINE.AMINOTRANSFERASE

2 3 4 5 6 7

Prostinol TrialLaboratory Measurements

Higher VI => Treatment Emergence

Code: splus80\users\prostinol\driverIORandForest.sscOutput: splus80\users\prostinol\labForest.sgr

Variable Importance (Inside Out Bagged Tree)

Lab Measurements


Regular Supervised Learning Models

Hyperkalemia Risk Example

Identify and assess clinical and demographic predictors of hyperkalemia (sK > 5.5 mmol/L) in antihypertensive (AH) trials

+ Data from multiple Phase III trials

Analysis dataset (over 34 000 observations) represents 4776 patients from 14 anti-HT drug trials

+ Low hyperkalemia prevalence: 186/4776 ~ 4%

Hyperkalemia is important risk factor in dysrhythmia and cardiac arrest, and can be instigated by AH drugs acting through RAS

Acknowledgement+ Vasily Belozeroff – Amgen; Charlie Barr – Roche; Drew Levy – Novartis

AGE, SEX, RACEBMIDIABETIC GROUP

DRUG – DOSE1DRUG – DOSE2ADD DRUGDURATION ON THERAPYINSULIN

CALCIUMSODIUMMAGNESIUMPOTASSIUM AT BASELINEPOTASSIUM AT PRIOR

MICROALBUMINSERUM ALDOSTERONERENIN TOTALRENIN DIRECTCREATININE CLEARANCE

Predictor Variables


Regression models fit: HK = f(predictors) + e+ Recursive partitioning + Logistic regression+ Neural net+ Naïve Bayes

Some ensemble models tried as well+ Bagging, boosting, random forests

HK cases oversampled to deal with low prevalence

Models compared with ROC in validation set+ All models fit validation set quite well+ Rpart and logistic regression predicted > 60% of positives in hold out set+ All models predicted > 85% of negatives

Models

Model Comparison

Several key explanatory variables

- Baseline potassium

- Creatinine Clearance

- Drug/Dose

- Magnesium

- Duration on therapy

- Calcium

The model is refined by dropping other variables

Relative importance of explanatory variables[ANOVA-style decomposition]

Variable Importance

Model Interpretation

Risk factors for hyperkalemia identified

+ Baseline potassium

+ Creatinine Clearance

+ Drug/Dose of concomitant meds

Actionable decision rules

Risk factors identified: Kbase, CreatClearance, Concomitant Meds

Model Interpretation


Hyperkalemia Risk Summary

75% of pts with baseline sK > 4.75 develop HK+ Expected result

20% of pts with baseline sK < 4.75 develop HK: these are pts who receive drug B, or high dosage of drugs A and E (40%), and have creatinine clearance < 63 (90%)

+ Interesting approach to identifying drug-drug interactions

6% of pts with baseline sK < 4.75 AND drug regimens other than the above develop HK: these are pts with creatinine clearance < 98 (15%) and sodium < 137 (80%)

25% of pts with baseline sK > 4.75 will NOT develop HK: these are pts NOT on high dosage of drugs A, E, D or low dosage of B (52%), AND have magnesium > 0.81 (72%)

Graphics to rapidly identify subjects with potential safety issues

+ Hy’s law: scatter plots, scatter plot matrix, patient profiles

+ Temporal/treatment causality: shift plots, patient profiles

Treatment emergence analysis at patient-level + Inside out machine learning for labs elevated on treatment

Models for specific lab elevations of concern+ Supervised learning models – hyperkalemia example

We really can do a lot better than tables !!!!!

2%25%Bilirubin(n=200)

12%35%AST(n=200)

15%40%ALT(n=200)

Elevations > 3XULNAny Elevation

Labs Analysis Summary


Clinical Review and Reporting

Monitors

Data Mgt

Clinical

Statistics

Statistics

Programming

Publishing

Medical Writing

Instream Unblinded

Statistics

Clinical

Management

ProtocolSAP

DataCleaning


CSRNDA

Labeling



Design

Interactive Clinical Review

Interactive Clinical Review on the iPhone


Web 2.0 – Social Networks

Users can express themselves+ Facebook, MySpace, Flickr, Blogs, …, Swivel, ManyEyes,

Emergent structures + Wikipedia+ Folksonomies e.g. youTube, Flickr, del.icio.us

Mashups+ "A mashup is a web application that seamlessly combines content from

more than one source into an integrated experience.“

“A lot of talk about Web 2.0, mashups, Ajax etc., which in my mind are all facets of the same phenomenon: that information and presentation are being separated in ways that allow for novel forms of reuse.”

Sho Kuwamoto

Social Networks for Review and Reporting


Social Networking for Review and Reporting

Create statistical graphs and review/report templates+ Dot plot, box plot, line plot, etc…

Re-use graphs and review/report templates across studies+ Graph templates – custom graphs

Re-style graphs/tables for publications and presentations+ Styles for journals and company power points

Consistent use of graphs, reviews, reports across organization

+ Exploratory, Review, Reporting, Presentation, Publication+ Statistics, Clinical, Data Management, Medical Writing, Management+ FDA gets the transparency it needs

Key Use Cases

Social Networking Example: Start with Basic Scatter Plot

Simple X-Y Scatter Plot of Lab Data

Start with Basic Scatter Plot

My Liver Lab Shift Plot

Customize with Reference Lines, Shift Line, Titles, Labels, Legend etc.

My Liver Lab Shift Plot - Metadata

Add Searchable Metadata to Enable Sharing

Liver Lab Shift Plot – Save Template

Save as Template for Re-use – with Customized UI

Liver Lab Shift Plot – Used for New Graph

New Graph – Choose Liver Lab Shift Plot Template !!

Liver Lab Shift Plot – Transferred, Applied to New Data


Summary

Safety data can be analyzed and presented clearly !!+ Exploratory analysis, review and publication reports

Statistical analysis - multiplicity / sparseness can be handled + Bayes hierarchical linear models + Supervised learning – inside-out

+ Bias-reduced regression (Jeffreys prior penalty in logistic regression)

Graphical analysis+ Adverse Events – dot plots, p-Risk plot, interval plots+ Lab measurements – targeted multivariate and shift plots

Rapid deployment to clinicians, DSMBs etc. is vital

Web 2.0 Technologies can jump Pharma into the future NOW !+ Online data review+ Reports, submissions, presentations

Monitors

Data Mgt

Clinical

Statistics

Statistics

Programming

Publishing

Medical Writing

Summary

Instream Unblinded

Statistics

Clinical

Management

ProtocolSAP

DataCleaning


CSRNDA

Labeling



Design

Happy, Productive, Effective Drug Development Team


Michael O’Connell Director, Life Science SolutionsInsightful Corp.

[email protected]

Contact Information

Amit, O. (2007). Understanding Patients Safety Through Use of Statistical Graphics. Insightful webcast. http://www.insightful.com/news_events/events.asp

Amit, O., Heiberger, R. and Lane, P. (2007). Graphical approaches to the analysis of safety data in clinical trials. Pharmaceut. Stat. In press.

Cleveland, W. (1993): Visualizing Data. Hobart Press.Ma, H., Zhou, K., Xia, A., Austin, M., Li, G., and O’Connell, M. (2007). Graphical Analyses of

Clinical Trial Safety Data. JSM 2007. http://www.insightful.com/news_events/2007jsm/Firth, D. (1993). Bias reduction of maximum likelihood estimates. Biometrika 80: 27-38O’Connell, M. (2006). Statistical modeling and graphical analysis of safety data in clinical trials.

Insightful webcast. http://www.insightful.com/news_events/events.aspO’Connell, M. (2006). Graphical analysis and reporting of safety data. 42nd DIA annual

meeting. http://www.insightful.com/news_events/events.asp

O’Connell, M. (2007). Statistical Graphics for Clinical Development Studies. 43rd DIA annual meeting. http://www.insightful.com/news_events/events.asp

Soukup, M. (2007). Visual Representations of Clinical Data during the NDA Review Cycle. 43rd

DIA annual meeting. http://www.insightful.com/news_events/events.aspO’Neill, R.T. (2005). Signal Detection in Clinical Trials: Some perspectives on New tools and

Processes - A Critical Path Update. 19th Annual DIA EuromeetingTufte, E. R. (1983). The Visual Display of Information, Graphics Press. Tukey, J. W. (1977). Exploratory Data Analysis. Addison-Wesley.Woodard, D., Jack, A., Hoffman, J and O’Connell, M. (2007). Bayesian Modeling with S-PLUS

and the S+flexBayes library. Proceedings of Phuse 2007.Zimmerman, HJ, 1978, Drug-Induced Liver Disease, In: Hepatotoxicity, The Adverse Effects of

Drugs and Other Chemicals on the Liver, 1st ed., pp. 351-3, Appleton-Century-Crofts, NY

ReferencesReferences

S-PLUS 8, Insightful Miner and BUGS+ S-PLUS 8 – package system, graphics, big data, eclipse workbench+ BUGS – OpenBUGS, WinBUGS, r2winbugs, brugs

Statistics+ S+flexBayes, Forest, Arbor, brlr

Graphics + GWE, Trellis, GOM, Graphlets

Deployment+ SPXML, rtfTools, pkReport+ ClinpackForSAS, SPLUSforSAS, Curl

Life Science Solutions+ Clinical Graphics (Report Graphics)+ Clinical Review (Review Graphics)+ PK/PD Reporting+ Safety+ Trial Design

Summary - Software

statistical modeling and graphical analysis of safety data in ... 2007...gastritis...

Documents