Literature DB >> 2691203

Comparison of Bayesian with group sequential methods for monitoring clinical trials.

L S Freedman1, D J Spiegelhalter.   

Abstract

We describe some problems with applying methods based on classical sequential analysis to monitoring clinical trials. A Bayesian method is developed and the boundaries are compared with frequentist schemes. For the examples chosen, the Bayesian boundaries can be quite similar to those obtained from Pocock and O'Brien and Fleming (OBF) rules, depending on the choice of prior distribution. They converge less rapidly than OBF's but more rapidly than Pocock's. In general the Bayesian methods provide the same desirable features as frequentist methods, without sacrificing flexibility and simplicity of interpretation.

Mesh:

Year:  1989        PMID: 2691203     DOI: 10.1016/0197-2456(89)90001-9

Source DB:  PubMed          Journal:  Control Clin Trials        ISSN: 0197-2456


  11 in total

1.  When to stop a clinical trial.

Authors:  S J Pocock
Journal:  BMJ       Date:  1992-07-25

2.  Bayesian design for two-arm randomized Phase II clinical trials with endpoints from the exponential family using multiple constraints.

Authors:  Wei Jiang; Jo A Wick; Jianghua He; Jonathan D Mahnken; Matthew S Mayo
Journal:  J Biopharm Stat       Date:  2017-11-27       Impact factor: 1.051

3.  Bayesian clinical trials at The University of Texas MD Anderson Cancer Center: An update.

Authors:  Rebecca S Slack Tidwell; S Andrew Peng; Minxing Chen; Diane D Liu; Ying Yuan; J Jack Lee
Journal:  Clin Trials       Date:  2019-08-26       Impact factor: 2.486

4.  A comparison of two worlds: How does Bayes hold up to the status quo for the analysis of clinical trials?

Authors:  Alice R Pressman; Andrew L Avins; Alan Hubbard; William A Satariano
Journal:  Contemp Clin Trials       Date:  2011-03-29       Impact factor: 2.226

5.  Bayesian sequential meta-analysis design in evaluating cardiovascular risk in a new antidiabetic drug development program.

Authors:  Ming-Hui Chen; Joseph G Ibrahim; H Amy Xia; Thomas Liu; Violeta Hennessey
Journal:  Stat Med       Date:  2013-12-16       Impact factor: 2.373

6.  Bayesian adaptive model selection for optimizing group sequential clinical trials.

Authors:  J Kyle Wathen; Peter F Thall
Journal:  Stat Med       Date:  2008-11-29       Impact factor: 2.373

7.  Sequential methods for random-effects meta-analysis.

Authors:  Julian P T Higgins; Anne Whitehead; Mark Simmonds
Journal:  Stat Med       Date:  2010-12-28       Impact factor: 2.373

8.  Interim analyses and stopping rules in cancer clinical trials.

Authors:  J Whitehead
Journal:  Br J Cancer       Date:  1993-12       Impact factor: 7.640

9.  Stopping rules, interim analyses and data monitoring committees.

Authors:  D Ashby; D Machin
Journal:  Br J Cancer       Date:  1993-12       Impact factor: 7.640

10.  Do we need to adjust for interim analyses in a Bayesian adaptive trial design?

Authors:  Elizabeth G Ryan; Kristian Brock; Simon Gates; Daniel Slade
Journal:  BMC Med Res Methodol       Date:  2020-06-10       Impact factor: 4.615

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.