Robertson, David S. and Choodari-Oskooei, Babak and Dimairo, Munya and Flight, Laura and Pallmann, Philip and Jaki, Thomas (2023) Point estimation for adaptive trial designs I: A methodological review. WILEY, HOBOKEN.
Full text not available from this repository. (Request a copy)Abstract
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 inmany 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.
| Item Type: | Other |
|---|---|
| Uncontrolled Keywords: | CONDITIONALLY UNBIASED ESTIMATION; SAMPLE-SIZE REESTIMATION; GROUP SEQUENTIAL DESIGNS; CLINICAL-TRIALS; CONFIDENCE-INTERVALS; 2-STAGE DESIGNS; SUPPLEMENTARY ANALYSIS; STATISTICAL-INFERENCE; SECONDARY PARAMETERS; TREATMENT SELECTION; adaptive design; bias-correction; conditional bias; flexible design; point estimation |
| Subjects: | 000 Computer science, information & general works > 004 Computer science |
| Divisions: | Informatics and Data Science > Department Machine Learning & Data Science > Lehrstuhl für Computational Statistics (Prof. Dr. Thomas Jaki) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 23 Apr 2024 13:25 |
| Last Modified: | 23 Apr 2024 13:25 |
| URI: | https://pred.uni-regensburg.de/id/eprint/60168 |
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