Bayesian clinical trials

By D. Berry. 2006

707 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Genetics, Statistics and Probability

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Citations in the corpus

Robust meta‐analytic‐predictive priors in clinical trials with historical control information (2014, positive): “In such settings, it seems attractive to use the historical control data in the design and analysis of the new trial (Berry, 2006), as fewer patients can then be randomized to control.”
Why Experimenters Might Not Always Want to Randomize, and What They Could Do Instead (2016, background): “A forceful argument for a Bayesian perspective on experimental design has been made by Berry (2006). A few examples of experiments from the recent political science literature were mentioned in the introduction. Additional examples abound. Bolsen et al. (2014) sent messages on water conservation to ”
Comparison of antiarrhythmic drug therapy and radiofrequency catheter ablation in patients with paroxysmal atrial fibrillation: a randomized controlled trial. (2010, methodology): “The protocol-defined primary statistical analysis of the primary end points was Bayesian.(19-21) Study success required a probability of superiority of catheter ablation over ADT in the primary end point at the final analysis of at least 98% to control the type I error rate at no more than 2.”
Hierarchical Commensurate and Power Prior Models for Adaptive Incorporation of Historical Information in Clinical Trials (2011, positive): “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).”
Results of a randomized study of 3 schedules of low-dose decitabine in higher-risk myelodysplastic syndrome and chronic myelomonocytic leukemia. (2007, neutral): “of courses requiring hospitalization (%) 50 (12) 14 (14) 23 (23)”
Bayesian estimation supersedes the t test. (2013, neutral): “Gelman, A. (2006).”
Machine learning applications in drug development (2019, background): “More broadly, several examples of successful Bayesian adaptive designs have emerged over the last 20 years, and we refer the reader to [63,82] for a survey.”
Sample size planning for statistical power and accuracy in parameter estimation. (2008, background): “…Mehta & Patel 2006), complications in planning subgroup analyses (e.g., Brookes et al. 2004, Lagakos 2006), the impact of noncompliance on power in randomized studies (e.g., Jo 2002), and Bayesian approaches to sample size planning (e.g., Berry 2004, 2006; Inoue et al. 2005; Lee & Zelen 2000).”
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (2014, background): “Berry (2006, 2011), Cavagnaro, Myung, Pitt, and Kujala (2010), and those cited by Roy, Ghosal, and Rosenberger (2009, p.”