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Samenvatting Lecture 4 - Clinical Trials and Clinical Development AM_1180 (AM_1180)
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Lecture 4ICH-E9→ separate guideline for statistical principles of clinical trials, not
procedures or methods. Talks about bias reduction, trial designs and trial
methods. Target audience is trial statisticians.
Aims:
- Primary late Phase trials, mainly confirmatory trials of efficacy
- For safety, PD and PK variables
- For integrated data across trials
- Relevant for earlier phase, exploratory trials.
ICH-E9 direction
● To minimize bias, the ways how we minimize bias is given in E9
● To maximize precision and evaluate robustness of results and conclusion (how much
data can withstand different ways of analysis/looking at it).
● Guidance refers to using frequentist methods (Bayesian and other approaches may
be considered)
There are many types of bias: interpretation bias, selection bias, confirmation bias.
Bias→ the systematic tendency of any factors associated with the design, conduct, analysis
and evaluation of the results of a clinical trial to make the estimate of a treatment effect
deviate from its true value. Bias introduced through deviations in conduct is referred to as
operational bias. The other sources of bias listed above are referred to as statistical.
Sources of bias
1. From design of the trial→ Assignment of treatments such that subjects at
lower risk are systematically assigned to one treatment. E.g. in an open-
label, somebody with a worse kidney function will get different treatment
or doctor could ask their patient to participate in a certain medicine trial
2. Arising during the conduct and analysis→ Protocol violations. Exclusion of
subjects from analysis based upon knowledge of subject outcomes
Robustness→ sensitivity of the overall conclusions to various limitations of the
data, assumptions, and analytic approaches to data analysis. Treatment effect
and primary conclusions do not substantially affect when analyses are carried out
based on alternative assumptions or analytic approaches.
Bayesian approaches→ Approaches to data analysis that provide a posterior probability
distribution for some parameter (e.g. treatment effect), derived from the observed data and a
prior probability distribution for the parameter. The posterior distribution is then used as the
basis for statistical inference.
Frequentist Methods→ Statistical methods, such as significance tests and confidence
intervals, which can be interpreted in terms of the frequency of certain outcomes occurring in
hypothetical repeated realizations of the same experimental situation.
Design factors→ randomization, blinding, trial design, control group
● Randomization→ process of assigning clinical trial participants to treatment
groups with the element of change. Avoids selection bias. Similar
, treatment groups. Only one factor (the treatment) is different between two
or more treatment groups, everything else is as same as possible. This is
however not possible (but we try to as much as possible).
○ Random allocation→ known change of receiving a treatment. Cannot
predict the treatment to be given. Codes are prepared before trial.
When blinded, codes are not broken before the end of trial (only in
case of emergency or if outcome needed for medical decisions).
○ Different ways to randomize→ random number tables, computer
programs. Methods for assigning randomisation number: IVRS,
sequential numbering.
○ Types of randomization→ simple, block(ed), unequal, stratified (block),
response adaptive (dynamic allocation)
○ Simple randomization→ treatments allocated to patients in a
completely random way. Could have imbalance in number per group
or trends in group assignment
○ Blocked randomization→ numbers allocated to each treatment are
equal after every block of subjects. Sequence of allocations within a
block is chosen randomly. Block size is the number of subjects
within 1 block so that randomisation fulfills the required distribution.
Number of subjects assigned to each treatment is not far out of
balance. Consequences for distribution of IP (investigation product) to
different sites, IP can only be distributed in blocks.
○ Unequal randomization→ unequal numbers of subjects in each arm.
Safety of greater importance, e.g. randomize 2 to 1 in favor of active
(compared to placebo)
○ Stratified randomization→ subjects are divided in subgroups based
on characteristics (age, gender, high/low risk groups), whereafter
randomization takes place. Can also do stratified block
randomisation: you have equal division of characteristics and blocks.
○ Responsive adaptive randomization→ Earlier participants got
assigned to treatment A, the chance that the next participant
assigned to treatment A is less, to balance it out.
● Blinding→ to avoid bias in measurements
○ Open-label→ no blinding. Use when blinding is not possible, pilot
trials, dose ranging trials. Totally different treatments, no placebo
for a treatment etc. could be reasons not to blind.
○ Single blind→ only investigator knows which treatment is given to
which subject. Used when double-blinding is not possible and when
laboratory values, AEs etc. reveal which treatment is given. For
example when the substance changes color of the treatment when
exposed to light, while placebo would not change color.
○ Double blind→ neither investigator nor subject know which
treatment is given. IP (reference and test treatment) identical in
taste, smell and appearance.
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