selecting a study population

+ Selecting a study population for clinical trials

Dr Greg FoxUniversity of Sydney, Australia

+Overview

Selecting a suitable study population to answer our research question: where? who’s in? who’s out?

Minimising biases in randomized trials

Case study: selecting a study population for a randomized study of LTBI treatment

+Part I:

Selecting a suitable study population

+ Specifying the study population

Total Population

Target population

Accessible population

Population (of the world) Population which the target population is hoped to represent

Target populationGroup from whom study population is drawn

Accessible population / Reference populationUsually defined by time & place

Population sample / Study populationSelected subset of the study population

Study population

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Who’s in and who’s out? Selecting a study population

Why select ? Rarely possible to study all target population

Criteria for selection: relevant to the study objectives; practicality (accessible); usually defined by time & place

Sources of study population: Community, workplace, school, hospital;

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Choosing the accessible population

Common options are: Clinic based Population based Hospital based

Each setting has its problems How might biases affect your results?

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Study population: select how?

Requires enumeration of population

Random sampling: Random: Each person (unit) has an equal chance Stratified random: Random samples from specified sub

groups

Systematic sampling: Use regular interval to sample (every 5th person)

Cluster: random sample of groups (households)

Convenience (‘grab’): easily accessed but not random

+How can you be sure your study sample is representative of the target population?

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+ Selecting the study setting and study population

Selecting a suitable study setting Setting is often determined by available clinical links or existing collaborations Some study questions may not be answerable in some settings (e.g. treatment for

DR-LTBI in a low-prevalence setting, complex interventions in weak health care setting)

Single site or multi-site recruitment ?

Defining a suitable study population to answer the research question Define study subjects precisely and unambiguously Consider whether to choose broad vs narrow selection criteria

Desired target population (e.g. household vs all ‘close’ contacts) Intended generalisability (e.g. adults vs all ages) High risk groups of particular clinical importance (e.g. solid organ transplant

recipients, PLHIV, children) Consider efficiency of recruitment (e.g. TST positive only vs all contacts)

+ Case study 1 : MDR-TB prevention among household contacts

We will illustrate the issues relating to selecting study populations through the design of a clinical trial of levofloxacin as treatment for latent TB infection among contacts of

MDR-TB patients

+ Background to V-QUIN MDR trial Treating active MDR-TB is complex, toxic and costly

Close contacts of people with MDR-TB have a high risk of developing TB1

Preventive therapy is routinely offered to infected contacts of people with drug-susceptible TB (isoniazid, rifampicin, isoniazid+rifapentine…)

There is not yet evidence from RCTs to determine whether preventive therapy may be effective in MDR-TB contacts

“There is an urgent need for a multicenter, randomized, controlled trial”… of preventive therapy2

1 – Kritski 1996; 2 - Schaaf et al, Paediatrics, 2002

Case study 1: MDR-TB prevention among household contacts

+ Research question

“What is the effectiveness of levofloxacin given for 6 months, compared to placebo, in the prevention of active TB among close contacts of patients with MDR-TB who have latent tuberculosis infection?”

Which settings and study populations could best answer this question?


+Study setting: requirements Availability of a sufficiently large

target population for recruitment

Capacity of health system to implement the study

Effective local engagement in research

Sufficient infrastructure to support technical aspects of the trial


+ Choosing study eligibility criteria

If criteria are too narrow: Unable to reach recruitment targets Results not generalizable to other important patient populations Recruitment process too complex

If criteria are too broad: May reduce average effect size (choosing some who may not

actually benefit) May include individuals less likely to comply (reducing follow-up) Proportion of eligible subjects recruited may be lower, with potential

for selection bias

Choosing a balance between1. Internal validity (ability to identify what is ‘true’ in the study population)

and2. Generalizability (external validity – an extension of the observed

results to a larger population)

+ Choosing inclusion criteriaInclusion criteria: example Any age [?] Living in the same household

as the index patient within the previous 3 months [? why not ‘close’ contacts]

TST result: Tuberculin skin test positive (a

size of 10mm or greater at first reading); OR

Any TST size if known to be HIV positive or severely malnourished; OR

New TST conversion on the second reading*

*defined as: (a) If the first test was <5mm: a size of 10mm or greater at second reading; OR (b) If the first test was 5-9mm: An increase of 6mm or greater at the second reading


• Minimize risk & enhance participant safety

• Select subjects likely to benefit from the intervention

• Include subjects for whom the intervention may be considered in future policy and praxis

• Use standard definitions

+ Choosing exclusion criteriaExclusion criteria: example A diagnosis of current active TB

disease made during initial assessment [?how]

Known to be pregnant Unable to take oral medication Documented previous treatment

for MDR-TB Dialysis-dependent chronic

kidney disease etc etc.


Consider excluding:• Those with clear, recognised

contraindications to the intervention

• Those highly unlikely to comply with trial protocol

• Those in whom the intervention may not be effective, and/or ethically justifiable

However, avoid unnecessary complexity and narrow criteria

+ An aside: Including children in TB trials

Barriers to including children in clinical trials for TB include Lack of pharmacokinetic and pharmacodynamic data Lack of appropriate drug formulations Concerns by IRBs and clinicians Lack of funding (2% of total TB drug research funding, 25% of need)1

Parental concerns

Consensus statement on child TB trials: « Children should be included in studies at the early phases of drug

development and be an integral part of the clinical development plan, rather than after approval »1

1Nachman et al, Towards early inclusion of children in tuberculosis drugs trials: a consensus statement. Lancet ID 2015

+ Case study 2: MDR prevention among liver transplant candidates

Torre-Cisneros, CID 2015

+Study design Multi-centre, prospective, non-inferiority RCT

comparing isonazid with levofloxacin in treatment of LTBI in patients eligible for liver transplantation 500mg daily levofloxacin for 9 months vs 300mg isoniazid for

9 months Target sample size 870 subjects to be randomized


Case study 2: MDR-TB prevention among liver transplant candidates

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Case study 2: MDR-TB prevention among liver transplant candidates

Inclusion criteria On the waiting list for solid organ

transplant within a network of Spanish hospitals

Aged ≥18 years No evidence of active TB One of:

Latent TB infection (TST ≥ 5mm or positive IGRA); or

History of ‘improperly treated TB’, or Recent TB contact, or Xray changes consistent with old TB

(apical nodules, calcified lymph nodes, pleural thickening)

+Results


+Results 33/33 LEV and 27/31 INH patients took steroids

2/33 LEV patients (6%) and 7/18 INH patients (38.9%) developed severe hepatotoxicity

6/33 LEV patients (18.2%) developed tenosynovitis, affecting knee in 5 and achilles tendon in 1, permanently discontinued in 5

Study terminated early: “Due to high frequency and intensity of this unexpected side effect the trial was definitively stopped” (?pre-defined stopping rules)


+ Considerations in selecting study populations Study population affects interpretation of findings

Generalizability of findings from Torre-Cisneros ? – determined by participant characteristics

Ensure the research question can be addressed within the intended study population

Clearly specify inclusion and exclusion criteria in detail

Consider generalizability of findings

Consider advantages vs disadvantages of multiple sites

+Part II:

Minimising bias inclinical trials

+Minimising bias in clinical trials

+Bias in clinical trialsOur goal in conducting clinical trials is to obtain valid

(‘truthful’) and precise (‘accurate’) estimates of the relationship between an intervention and outcome

The main threats to validity are caused by bias: a tendency of an estimate to deviate in one direction from a true value Leading to underestimation or overestimation of the effect of

the intervention It is impossible to know for sure whether a clinical study is

biased, as we cannot know ‘the truth’

+Important forms of biasKey forms of bias in clinical trials include:

Confounding (an ‘imbalance’ between groups that may be systematic, or by chance)*

Selection bias (selection for an intervention is based upon the outcome)

Information bias (measurement error in the exposure, outcome or covariates = ‘misclassification’**)

How can we minimise biases in clinical trials?

*Confounders can be describe as variables that are: (a) Independently predictive of disease, within strata of exposure, (b) Associated with the exposure, (c) Not an intermediate in the causal pathway between exposure and outcome ** Misclassification bias for categorical variables

+RandomizationRandomization is the random allocation of an individual or group to an intervention

• Each individual theoretically has the same opportunity to be assigned to each of the study groups

• If done properly, randomization can ensure study groups are balanced - for both measured and unmeasured factors (confounders)

• Randomization can satisfy assumptions required by statistical methods (e.g. independence between observations, no unmeasured confounding)

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Viera, Fam Med 2007

+Key components of adequate randomization:

1. Truly random sequence generation

✓ ✗- Computer generated- Random numbers tables- Draw numbers from a hat- Toss a coin

- Recruiting on alternate days to each group

- Assigning random letter by last name

- Hospital chart numbers - Day of the week

Randomization is good at achieving balance in measured and unmeasured covariates

+ e.g.

+ Covariate balance with randomization

Sterling et al, NEJM 2011

+Key components of adequate randomization

2. Allocation concealmentKeeps the group to which the study subjects are assigned unknown, or easily ascertained, up to the point that study participants are given the intervention.

Aims to avoid bias in treatment allocation (selection)

Inadequate allocation concealment can increase effect estimates by as much as 40%1

1Schultz et al. Empirical evidence of bias. JAMA 1995

+Blinding

+ Key components of adequate randomization:

3. Blinding• Blinding all concerned to the intervention group can

reduce ascertainment bias1.

• The best way to reduce ascertainment bias is to keep all participants and investigators in the study blinded as long as possible.

• Blinding is not always possible, by nature of the intervention (e.g. surgery for MDR-TB – although sham surgery possible)

1Ascertainment bias occurs when the results or conclusions of a trial are systematically distorted by knowledge of which intervention each participant is receiving. 2Schultz et al. Empirical evidence of bias. JAMA 1995

+Levels of blinding Single Blind: Subject is not aware of group

allocation

Double Blind: Neither the subjects nor treating staff know group allocation

Triple-Blind: Neither subjects, investigators nor data analysts and monitoring committee know group allocation

+Randomization methods Randomization units

Simple (e.g. coin toss, simple random numbers) Block (fixed or variable block sizes)

Predicts against investigators predicting sequence if block size is 4, there are 6 combinations: AABB, ABAB,

BAAB, BABA, BBAA, and ABBA.

Stratified randomization (randomize within each stratum, to reduce variability in group comparison)

Cluster randomization (groups of individuals, e.g. households) – we will discuss later

1:1 randomization most often, but can use other ratiosn

Each approach has advantages and disadvantages (consult with your trial statistician early)Altman DG, Bland JM. How to randomize. BMJ 1999

Efird J. Blocked randomization. Int J Environ Res Pub Health 2011

+Part III:

Example of choosing appropriatesample size

+Sample size calculations The sample size is the expected number of participants

required to adequately answer the research question

Sample size is clinically and ethically important Too few subjects: may prevent valid and precise

determination of the treatment effect; may incur excessive cost and time

Too many subjects: may expose more individuals to risk

Before embarking upon the sample size calculation, you need to determine the planned primary outcome measure and measures of interest.

What is the clinically important difference?

+Clinically important difference and

confidence intervals

No important effect

Inconclusive, needsfurther study

Clinically important

Small but unimportant effect

At least a small effect. May be Important. Needs further study δ

+ Example: sample size for V-QUIN trial

Sample size calculations require explicit decisions, including: Study design (e.g. superiority / non-inferiority; individual or

cluster randomization; stratified effects required) Statistical analytic method planned (usually frequentist;

could use Bayesian methods) Outcome measures (relative risk, risk difference etc) Thresholds for type I (e.g. α = 0.05) and type II (1- β = 0.8)

errors Minimum clinically important difference (δ) (prior slide) Precision of the estimates (standard deviations in each

group, σ) Expected event rates (e.g. TB incidence) based on other

studies Expected recruitment and drop-out rates

+ Sample size example for binary outcomes

Schlesselman (1974) - Sample size requirements in cohort and case control studies of disease, American Journal of Epidemiology 99, 381-384.

Can use PS Power (which applies this formula)

A good illustration of using this formula is given in Moore and Joseph, Lupus (1999) 8: 612-619

+ Sample size example

Parameter ValueZ(1-α/2) for alpha = 0.05 1.96Z(1-β) for beta = 0.2 0.84P1 - proportion in control arm 0.03P2 - Proportion in active intervention arm 0.009

n (in each group) prior to adjustment 680Additional adjustments 1.106Design effect (clustering) 1.106% loss to follow-up 10%Fluoroquinolone resistance 16.7%

Number randomized in both groups 2006Number contacts assuming 60% TST+ 3344Index patients assuming 2.1 index patients / contact 1592

+Sample size calculationsOther considerations:

Cost Event rate Feasibility

Sample size calculations are covered well in many places:

Moore AD, Joseph L. Sample size considerations for superiority trials in systemic lupus erythematosus. Lupus, 1999.

Joseph L. Bayesian and mixed Bayesian likelihood criteria for sample size determination. Stat Med 1997.

Zou KH, Normand S-L T. On determination of sample size in hierarchical binomial models. Stat Med 2001.

+ Acknowledgements

Vietnam National Tuberculosis ProgramA/Prof Nguyen Viet NhungA/Prof Dinh Ngoc Sy

Pham Ngoc Thach Hospital

An Giang, Binh Dinh, Ca Mau Can Tho, Da Nang, Ha Noi, Tien Giang, Ho Chi Minh City, Vinh Phuc Tuberculosis Programs

Australian National Health and Medical Research Council (NHMRC)

Woolcock Institute of Medical Research, SydneyDr Carol Armour, Director and staff

Woolcock Institute of Medical Research, VietnamDr Nguyen Thu Anh

And, most importantly, the people of the participating provinces

selecting a study population

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