Adaptive design: estimation and inference with censored data in a semiparametric model

Point estimation for adaptive trial designs I: A methodological review

Recent FDA guidance on adaptive clinical trial designs defines bias as "a systematic tendency for the estimate of treatment effect to deviate from its true value," and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. The conventional end-of-trial point estimates of the treatment effects are prone to bias in many adaptive designs, because they do not take into account the potential and realized trial adaptations. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received relatively less attention. This article is the first in a two-part series that studies the issue of potential bias in point estimation for adaptive trials. Part I provides a comprehensive review of the methods to remove or reduce the potential bias in point estimation of treatment effects for adaptive designs, while part II illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. The methods reviewed in this article can be broadly classified into unbiased and bias-reduced estimation, and we also provide a classification of estimators by the type of adaptive design. We compare the proposed methods, highlight available software and code, and discuss potential methodological gaps in the literature.

An adaptive group sequential phase II design to compare treatments for survival endpoints in rare patient entities

For rare diseases, standard treatments are often not available and essential study parameters are difficult or impossible to obtain. Therefore, designs of clinical trials for these diseases are often based on little information. Adaptive designs allow such trials to be started and to gain information during the study. Motivated by a trial for a rare subtype of renal-cell carcinoma, we present a two-stage adaptive design for right-censored time-to-event data and a two-sided test. After the first stage, one can stop for futility or continue with reestimated sample size. The properties of such designs are analyzed by simulation studies.

Adaptive trial design: a general methodology for censored time to event data

Adaptive designs allow a clinical trial design to be changed according to interim findings without inflating type I error. The Inverse Normal method can be considered as an adaptive generalization of classical group sequential designs. The use of the Inverse Normal method for censored survival data was demonstrated only for the logrank statistic. However, the logrank statistic is inefficient in the presence of nuisance covariates affecting survival. We demonstrate, how the Inverse Normal method can be applied to Cox regression analysis. The required independence between test statistics of the different stages of the trial can be obtained by two different approaches. One is using the independent increment structure of the score process. The other uses right censoring and left truncating to divide individuals follow-up into per-stage data. Simulation studies show, that performance of the adaptive design does not depend on the method used for obtaining independence. Either way, an adaptive Cox regression analyis is more efficient than an adaptive logrank analysis if nuisance covariates affect survival.

Efficient ways exist to obtain the optimal sample size in clinical trials in rare diseases

OBJECTIVE

Recruitment of pediatric patients in randomized clinical trials is hampered by the rarity of many conditions and by ethical constraints. The objective of this paper is to give an overview of design options to obtain a statistically valid result while including a minimum number of subjects.

STUDY DESIGN AND SETTING

Overview and discussion of several approaches to conduct valid randomized clinical trials in rare diseases and vulnerable populations.

RESULTS

Sequential designs have been developed as efficient ways to evaluate accumulating information from a clinical trial, thereby reducing the average size of trials. Different sequential procedures exist, including group sequential designs, boundaries designs, and adaptive designs. The sample size attained at the end of the trial is unknown at the start. The sample size for a given set of alpha, beta, and effect size may turn out to be larger than with a classical fixed sample size approach. Simulations have shown that on average, sample sizes are smaller.

CONCLUSION

There are several possibilities to optimize the number of subjects in a clinical trial. The rarity of many disorders in children and the ethical requirements in this patient population should not obstruct the performance of well-designed research to support clinical decision making.