could someone explain what is the source of bias of treatment effect estimation in context of adaptive designs?
The FDA guidance for industry for adaptive designs https://www.fda.gov/media/78495/download has the following paragraph
It is important that clinical trials produce sufficiently reliable treatment effect estimates to facilitate an evaluation of benefit-risk and to appropriately label new drugs, enabling the practice of evidence-based medicine. Some adaptive design features can lead to statistical bias in the estimation of treatment effects and related quantities. For example, each of the two cases of Type I error probability inflation mentioned in section III.A. above has a potential for biased estimates. Specifically, a conventional end-of-trial treatment effect estimate such as a sample mean that does not take the adaptations into account would tend to overestimate the true population treatment effect. This is true not only for the primary endpoint which formed the basis of the adaptations, but also for secondary endpoints correlated with the primary endpoint. Furthermore, confidence intervals for the primary and secondary endpoints may not have correct coverage probabilities for the true treatment effects
is my understanding correct that bias might arise only when trial is stopped early? or there is also room for bias when trial continues till the end (providing multiplicity adjustment is in place)? For example, the target sample size is 200 and the trial terminates after 25 patients because of a significant difference between treatment groups, you recognize the potential for a lot of bias in this situation. The earlier the decision, the larger the bias. Is my understanding correct of there is a source for other type of bias here?
