+
Selecting a study population
for clinical trials
Dr Greg Fox
University of Sydney, Australia
+
Overview
 Selecting
a suitable study population to
answer our research question: where?
who’s in? who’s out?
 Minimising
 Case
biases in randomized trials
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
Study
population
Population (of the world)
Population which the
target population is hoped
to represent
Target population
Group from whom study
population is drawn
Accessible population /
Reference population
Usually defined by time &
place
Population sample /
Study population
Selected subset of the
study population
+ 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;
5
+
Choosing the accessible population
 Common
options are:
 Clinic
based
 Population based
 Hospital based
 Each
 How
setting has its problems
might biases affect your results?
6
+ 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
7
+
8
How can you be sure your study
sample is representative of the
target population?
+ 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
Case study 1: MDR-TB prevention among household contacts
+
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?
Case study 1: MDR-TB prevention among household contacts
+
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 between
1. Internal validity (ability to identify what is ‘true’ in the study population)
and
2. Generalizability (external validity – an extension of the observed
results to a larger population)
Case study 1: MDR-TB prevention among household contacts
+
Choosing inclusion criteria
• 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
Inclusion 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
Case study 1: MDR-TB prevention among household contacts
+
Choosing exclusion criteria
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
Exclusion 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.
+
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
Case study 2: MDR-TB prevention among liver transplant candidates
+
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
Torre-Cisneros, CID 2015
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)
Torre-Cisneros, CID 2015
+
Results
Torre-Cisneros, CID 2015
+
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)
Torre-Cisneros, CID 2015
+ Considerations in selecting study
populations
 Study
population affects interpretation of findings
of findings from Torre-Cisneros ? –
determined by participant characteristics
 Generalizability
 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 in
clinical trials
+
Minimising bias in clinical trials
+
Bias in clinical trials
 Our
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 bias
 Key
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
+
Randomization
Randomization 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)
+
Viera, Fam Med 2007
+
Key components of adequate
randomization:
1. Truly random sequence generation
✓
-
✗
Computer generated
- Recruiting on alternate
Random numbers tables
days to each group
Draw numbers from a hat - Assigning random letter
Toss a coin
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 concealment
Keeps 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
Altman DG, Bland JM. How to randomize. BMJ 1999
your trial statistician early)
Efird J. Blocked randomization. Int J Environ Res Pub Health 2011
+
Part III:
Example of choosing
appropriate
sample 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, needs
further 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
1,
expected proportion in the treatment group. These
or + Sample
estimates size
can beexample
based on for
a pilot
study or the best
binary
available literature. Under the null hypothesis, we
outcomes
o
assume H0 : P0 ˆ P1 ˆ P, say. Unlike the normal
n- distribution,
the variance
binomial parameter
Schlesselman (1974)
- Sample of
sizearequirements
in cohort andis
of
entirely
determined
by the proportion
of outcomes,
P,
case control
studies of disease,
American Journal
of
Epidemiology
99, 381-384.
or
so
that it does
not need to be separately speci®ed.
Type
I (which
error applies
a, a Type
II error b, and the
d Given
Can useaPS
Power
this formula)
a expected proportions in the treatment and control
A good illustration
of using
this formula is given in Moore and
w groups,
P0 and P
1, respectively, we can calculate the
Joseph, Lupus (1999) 8: 612-619
n
sample
size as follows:
n
Nˆ
p ••••••••••••••••••••
re
p ••••••••••••••••••••••••••••••••••••••••••••••• 2
Z1 a=2 2P…
1 P†‡ Z1 b P0…
1 P0†‡ P1…
1 P1†
a
a
…
P1 P0†2
h
…
2†
al
m
where P is the average of the expected rates in the
+ Sample size example
Parameter
Z(1-α/2) for alpha = 0.05
Z(1-β) for beta = 0.2
P1 - proportion in control arm
P2 - Proportion in active intervention arm
n (in each group) prior to adjustment
Additional adjustments
Design effect (clustering)
% loss to follow-up
Fluoroquinolone resistance
Number randomized in both groups
Number contacts assuming 60% TST+
Index patients assuming 2.1 index patients /
contact
Value
1.96
0.84
0.03
0.009
680
1.106
1.106
10%
16.7%
2006
3344
1592
+
Sample size calculations
Other 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 Program
A/Prof Nguyen Viet Nhung
A/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, Sydney
Dr Carol Armour, Director and staff
Woolcock Institute of Medical
Research, Vietnam
Dr Nguyen Thu Anh
And, most importantly, the people of
the participating provinces