Abstract
During the development of new therapies, it is not uncommon to test whether a new treatment works better than the existing treatment for all patients who suffer from a condition (full population) or for a subset of the full population (subpopulation). One approach that may be used for this objective is to have two separate trials, where in the first trial, data are collected to determine if the new treatment benefits the full population or the subpopulation. The second trial is a confirmatory trial to test the new treatment in the population selected in the first trial. In this paper, we consider the more efficient two‐stage adaptive seamless designs (ASDs), where in stage 1, data are collected to select the population to test in stage 2. In stage 2, additional data are collected to perform confirmatory analysis for the selected population. Unlike the approach that uses two separate trials, for ASDs, stage 1 data are also used in the confirmatory analysis. Although ASDs are efficient, using stage 1 data both for selection and confirmatory analysis introduces selection bias and consequently statistical challenges in making inference. We will focus on point estimation for such trials. In this paper, we describe the extent of bias for estimators that ignore multiple hypotheses and selecting the population that is most likely to give positive trial results based on observed stage 1 data. We then derive conditionally unbiased estimators and examine their mean squared errors for different scenarios.©2015 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd.
Highlights
In drug development, it is not uncommon to have a hypothesis selection stage followed by a confirmatory analysis stage
An adaptive seamless designs (ASDs) is more efficient than having two separate trials because, as data from both stages of an adaptive seamless trial are used in the final confirmatory analysis, for the same power, fewer patients would be required in stage 2 of an adaptive seamless trial than in the setting with two separate trials saving resources
In order to make testing of new interventions more efficient, ASDs have been proposed. Such designs have been used for trials with subpopulation selection
Summary
It is not uncommon to have a hypothesis selection stage followed by a confirmatory analysis stage. Subpopulation (subgroup) analysis has been considered in many trials, encompassing many disease areas such as Alzheimer’s [20], epilepsy [21] and cancer [22] Most of these trials are single stage but investigators are beginning to design two-stage adaptive seamless trials for subpopulation selection such as the trial described in [23]. Methods for hypothesis testing in two-stage adaptive seamless trials with subpopulation selection that control type I error rate have been developed [5, 23, 26]. Some of these methods were initially developed for hypothesis testing following treatment selection.
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