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Robertson DS, Choodari‐Oskooei B, Dimairo M, Flight L, Pallmann P, Jaki T. Point estimation for adaptive trial designs I: A methodological review. Stat Med 2023; 42:122-145. [PMID: 36451173 PMCID: PMC7613995 DOI: 10.1002/sim.9605] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 10/21/2022] [Accepted: 11/01/2022] [Indexed: 12/02/2022]
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 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.
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Affiliation(s)
| | | | - Munya Dimairo
- School of Health and Related Research (ScHARR)University of SheffieldSheffieldUK
| | - Laura Flight
- School of Health and Related Research (ScHARR)University of SheffieldSheffieldUK
| | | | - Thomas Jaki
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
- Faculty of Informatics and Data ScienceUniversity of RegensburgRegensburgGermany
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Jaki T, Gordon A, Forster P, Bijnens L, Bornkamp B, Brannath W, Fontana R, Gasparini M, Hampson LV, Jacobs T, Jones B, Paoletti X, Posch M, Titman A, Vonk R, Koenig F. Response to comments on Jaki et al., A proposal for a new PhD level curriculum on quantitative methods for drug development. Pharm Stat 17(5):593-606, Sep/Oct 2018., DOI: https://doi.org/10.1002/pst.1873. Pharm Stat 2019; 18:284-286. [PMID: 30868716 DOI: 10.1002/pst.1942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 02/20/2019] [Indexed: 11/10/2022]
Affiliation(s)
- Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Allan Gordon
- Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Pamela Forster
- Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom
| | | | | | - Werner Brannath
- KKSB and IfS Faculty 3 - Mathematics/ComputerScience, University of Bremen, Bremen, Germany
| | - Roberto Fontana
- Department of Mathematical Sciences, Politechnico di Torino, Turin, Italy
| | - Mauro Gasparini
- Department of Mathematical Sciences, Politechnico di Torino, Turin, Italy
| | | | - Tom Jacobs
- Janssen Pharmaceutica N.V., Beerse, Belgium
| | | | - Xavier Paoletti
- INSERM CESP-OncoStat Institut Gustave Roussy & Université Paris-Saclay UVSQ & Service de Biostatistique etd' Epidémiologie, Gustave Roussy, Villejuif, France
| | - Martin Posch
- Center for Medical Statistics, Informatics, and Intelligent Systems; Medical University Vienna, Vienna, Austria
| | - Andrew Titman
- Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom
| | | | - Franz Koenig
- Center for Medical Statistics, Informatics, and Intelligent Systems; Medical University Vienna, Vienna, Austria
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