Bayesian Adaptive Methods for Clinical Trials

By Scott M. Berry, Bradley P. Carlin, J. J. Lee, P. Müller. 2010

560 citations recorded by the source corpus.

Statistical Approaches for Clinical Trials Introduction Comparisons between Bayesian and frequentist approaches Adaptivity in clinical trials Features and use of the Bayesian adaptive approach Basics of Bayesian Inference Introduction to Bayes' theorem Bayesian inference Bayesian computation Hierarchical modeling and metaanalysis Principles of Bayesian clinical trial design Appendix: R Macros Phase I Studies Rule-based designs for determining the MTD Model-based designs for determining the MTD Efficacy versus toxicity Combination therapy Appendix: R Macros Phase II Studies Standard designs Predictive probability Sequential stopping Adaptive randomization and dose allocation Dose ranging and optimal biologic dosing Hierarchical models for Phase II designs Decision theoretic designs Case studies: BATTLE and ISPY-2 Appendix: R Macros Phase III Studies Introduction to confirmatory studies Bayesian adaptive confirmatory trials Arm dropping Modeling and prediction Prior distributions and the paradigm clash Phase III cancer trials Phase II/III seamless trials Case study: Ablation device to treat atrial fibrillation Appendix: R Macros Special Topics Incorporating historical data Equivalence studies Multiplicity Subgroup analysis Appendix: R Macros References Author Index Subject Index

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/82401aadf62acd844e8bdde8a06d2b743ee9df7b

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Citations in the corpus

Spatial and spatio-temporal models with R-INLA. (2013, background): “During the last three decades, Bayesian methods have developed greatly and are now widely established in many research areas, from clinical trials (Berry et al., 2011), to health economic assessment (Baio, 2012) to the social sciences (Jackman, 2009), to epidemiology (Greenland, 2006).”
Hierarchical Commensurate and Power Prior Models for Adaptive Incorporation of Historical Information in Clinical Trials (2011, unknown): “Such adaptive borrowing is consistent with recent arguments on behalf of the ease and desirability of adaptivity in Bayesian clinical trials generally (Berry, 1993, 2006; Berry et al., 2010).”
Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research (2014, background): “, minimum required sample size is recalculated, or a treatment arm is abandoned) as data is gathered during the conduct of the trial [82].”
Adaptive designs in clinical trials: why use them, and how to run and report them (2018, methodology): “While this paper focuses on frequentist (classical) statistical methods for trial design and analysis, there is also a wealth of Bayesian ADmethods [100] that are increasingly being applied in clinical research [23].”
Chlamydia, gonorrhoea, trichomoniasis and syphilis: global prevalence and incidence estimates, 2016 (2019, methodology): “Page 4 of 43 other nine estimation regions, we calculated a summary prevalence estimate by meta-analysis if there were three or more data points.(12) There were sufficient data to generate an estimate for”
Robust meta‐analytic‐predictive priors in clinical trials with historical control information (2014, methodology): “Adaptive trials can minimize this risk (Hobbs, Carlin, and Sargent, 2013), since they allow to change features of the design based on interim analyses (Berry et al., 2010).”
Approximate Models and Robust Decisions (2014, background): “Note that it may be sensible to match the prior to the decision one hopes to reach; the prior should represent “an adversary who will need to be disillusioned by the data to stop further experimentation” Spiegelhalter, Freedman and Parmar (1994), as quoted in Berry et al. (2011).”
Machine learning applications in drug development (2019, background): “Such instances of potential in silico guiding of wet-lab experiments can be found in [61–63]; in particular, [62,63] describe the application of Bayesian multi-armed bandit methods to Phase I studies.”
Designing a Pilot Sequential Multiple Assignment Randomized Trial for Developing an Adaptive Treatment Strategy (2012, unknown): “It is important to understand that the SMART is not an adaptive trial design [41, 42].”
Using Bayes to get the most out of non-significant results (2014, background): “Figure 2 shows the four principles of inference by intervals (adapted from Freedman and Spiegelhalter, 1983; Serlin and Lapsley, 1985; Rogers et al., 1993; Kruschke, 2010a; Berry et al., 2011).”
Global Estimates of the Prevalence and Incidence of Four Curable Sexually Transmitted Infections in 2012 Based on Systematic Review and Global Reporting (2015, methodology): “Statistical methods such as hierarchical Bayesian modelling [20] can provide the tools to combine and explore sources of uncertainty in data from different sources.”
The Power Prior: Theory and Applications (2015, background): “Books illustrating the use of the power prior in epidemiological studies and clinical trials contexts include [25, 26].”