This document discusses key considerations for clinical trial design, size, and study population. It outlines common trial designs like parallel group, crossover, and factorial designs. Appropriate study design and adequate sample size are important to achieve study objectives and answer key questions. Sample size calculations should account for the primary endpoint, expected treatment effect, variability, type I and II errors. Selection of subjects and controls also impacts trial validity. An independent data monitoring committee provides trial oversight.
Clinical trial design, Trial Size, and Study Population
1. Clinical Trial Design, Trial Size, and Study
Population
Shubham A. Chinchulkar (Regulatory Affairs)
M.Tech (Pharm.)
National Institute of Pharmaceutical education and
Research (NIPER)
shubhamchinchulkar007@gmail.com
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2. The appropriate study design should be chosen to provide the desired information
Examples for study design :-
1. Parallel group
The most common clinical trial design for confirmatory trials in which each arm being allocated different treatment
Two separate treatment arms, A and B, are given so that one group receives only treatment arm A while another group
receives only treatment arm B
It include the investigational product at one or more doses and one or more control treatments (placebo or comparator)
In case of other design additional features (covariates, repeated measurements over time, interactions between design
factors, protocol violations, dropouts (see Glossary) and withdrawals) of trial complicate the analysis and interpretation
Clinical Trial Design
Screening
Treatment A
Treatment B
Treatment C
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3. 2. Crossover Design
Each subject is randomised to a sequence of two or more treatments and hence acts as his own control for treatment
comparisons
It reduces the number of subjects
Usually the number of assessments needed to achieve a specific power
2×2 crossover design - subject receives each of two treatments in randomised order in two successive treatment periods,
often separated by a washout period
To demonstrate the bioequivalence of two formulations of the same medication
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4. Variations like each subject receives a subset of n(>2) treatments or ones in which treatments are repeated within a
subject
In the 2×2 design the carryover effect cannot be statistically distinguished from the interaction between treatment and
period and the test for either of these effects lacks power because the corresponding contrast is 'between subject‘
It is important to avoid carryover (he effect of the treatment from the previous time period on the response at the
current time period)
Adequate knowledge of both the disease area and the new medication will helps to avoid carryover
The disease under study should be chronic and stable
The relevant effects of the medication should develop fully within the treatment period and washout periods should be
sufficiently long
Complications of analysis and interpretation arising from the loss of subjects, carryover leads to difficulties in assigning
adverse events
When losses of subjects from the trial are expected to be small, then only we can use crossover design
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5. 3. Factorial Designs
Two or more treatments are evaluated simultaneously through the use of varying combinations of the treatments
The simplest example is the 2×2 factorial design in which subjects are randomly allocated to one of the four possible
combinations of two treatments, A and B say
A alone; B alone; both A and B; neither A nor B
Specific purpose of examining the interaction of A and B
The statistical test of interaction may lack power to detect an interaction if the sample size was calculated based on the
test for main effects
It is important when this design is used for examining the joint effects of A and B, in particular, if the treatments are
likely to be used together
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6. It is use to establish Dose-response characteristics of the simultaneous use of treatments C and D
In some cases 2x2 design may be used to make efficient use of clinical trial subjects
This Strategy has proved to be particularly valuable for very large mortality trials
4. Dose escalation - The guiding principle for dose escalation in phase I trials is to avoid exposing too many
patients to subtherapeutic doses while preserving safety and maintaining rapid accrual
5. Fixed dose-dose response - To characterize the dose response
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7. To achieve clinical study objective –
a. Appropriate comparators (Reference product)
b. Adequate numbers of subjects
Primary and secondary endpoints and plans for their analyses should be clearly stated
The protocol should specify procedures for the follow-up of patients who stop treatment prematurely
3.2.2.1 Selection of subjects
Stage of development and the indication to be studied are the crucial parameters to select the subjects in clinical trials
E.g. Normal healthy subjects, cancer patients or other special populations in early phase development
In early stages of clinical trials the variability of groups of patients or healthy volunteers studied is limited to a narrow
range by strict selection criteria
Further, as drug development proceeds the selection get broadened
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8. Trial subjects should not participate concurrently in more than one clinical trial but there can be justified exceptions
Subjects should not be enrolled repetitively in clinical trials without time off treatment adequate to protect safety and
exclude carry-over effects
Women of childbearing potential should be using highly effective contraception to participate in clinical trials
For male subjects, potential hazards of drug exposure in the trial to their sexual partners or resulting progeny should be
considered
When drugs involved in trials are potentially mutagenic or toxic to reproductive system, then appropriate contraception
provision should be include
3.2.2.2 Selection of Control Group
Trials should have an adequate control group
Comparison may be made with placebo, active controls or diff. doses of drug under investigation
The objective of the trial decides the comparator
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9. 3.2.2.3 Number of subjects
The size of a trial is affected by -
a. Disease to be investigated
b. Objective of the study
c. The study endpoints
Statistical assessments of sample size should be based
on -
I. Expected magnitude of the treatment effect
II. Variability of the data
III. The specified (small) probability of error
IV. Secondary endpoints
Generally larger database is required to establish the safety of drug
Always be large enough to provide a reliable answer to the questions addressed
Primary objective of the trial responsible to decide subject number
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10. Following points should be specified for determining the sample size –
1. Primary variable – Primary Objective
2. Test statistic – T-test
3. Null hypothesis - The population mean of the treatment and control groups is assumed to be the same until an effect
estimate (which reaches some prespecified statistical threshold) is observed
4. The probability of erroneously rejecting the null hypothesis (the type I error)
5. Probability of erroneously failing to reject the null hypothesis (the type II error)
6. Approach to dealing with treatment withdrawals and protocol violations
The method by which the sample size is calculated should be given in the protocol and basis for calculation also should
be provided
The deviations from these assumptions are also important in sample size calculation
In confirmatory trials, assumptions should normally be based on published data or on the results of earlier trials
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11. Sample size calculations should refer to the number of subjects required for the primary analysis
If this is the full analysis set, the effect size need to reduce compared to the protocol
The exact sample size in a group sequential trial cannot be fixed in advance because it depends upon the play of chance in
combination with the chosen stopping guideline and the true treatment difference
When event rates are lower than anticipated or variability is larger than expected, methods for sample size re-estimation
are available without unbinding data or making treatment comparisons
Role of Independent Data Monitoring Committee (IDMC)
It is established by the sponsor to assess at intervals the progress of a clinical trial, safety data, and critical efficacy
variables and recommend to the sponsor whether to continue, modify or terminate a trial
IDMC should have written operating procedures and maintain records of all its meetings, including interim results;
these should be available for review when the trial is complete
The IDMC is a separate entity from an Institutional Review Board (IRB) or an Independent Ethics Committee (IEC)
IDMC include clinical trial scientists knowledgeable in the appropriate disciplines including statistics
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