Improving Bayesian methods for clinical trials

Author(s):  
Sylvie Chevret ◽  
Leslie Pibouleau
2016 ◽  
Vol 14 (1) ◽  
pp. 78-87 ◽  
Author(s):  
Caroline Brard ◽  
Gwénaël Le Teuff ◽  
Marie-Cécile Le Deley ◽  
Lisa V Hampson

Background Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment effect was based on historical data in only four trials. Decision rules were pre-defined in eight cases when trials used Bayesian monitoring, and in only one case when trials adopted a Bayesian approach to the final analysis. Conclusion Few trials implemented a Bayesian survival analysis and few incorporated external data into priors. There is scope to improve the quality of reporting of Bayesian methods in survival trials. Extension of the Consolidated Standards of Reporting Trials statement for reporting Bayesian clinical trials is recommended.


2005 ◽  
Vol 24 (14) ◽  
pp. 2183-2196 ◽  
Author(s):  
Mourad Tighiouart ◽  
André Rogatko ◽  
James S. Babb

1996 ◽  
Vol 30 (5) ◽  
pp. 460-465 ◽  
Author(s):  
Xiang Y Su ◽  
Alain Li Wan Po

OBJECTIVE: TO compare an empirical Bayesian, a fully Bayesian, and a classical fixed-effect (Peto) method for pooling event rates from separate epidemiologic studies or clinical trials. DESIGN: Four data sets used in meta-analyses by previous authors were evaluated. The first data set concerned death rates observed in clinical trials of beta-blockers, the second to lung cancer and smoking in 14 case-control studies, the third to drowsiness induced by the antihistamine compound chlorpheniramine, and the fourth to the use of intravenous magnesium in patients with suspected myocardial infarction. Randomly chosen data points were made more extreme to test the methods further. MAIN OUTCOME MEASURES: Pooled estimates of effect expressed as odds ratios and their associated 95% confidence intervals. RESULTS: All three methods gave comparable results with respect to the 95% confidence interval, although the Bayesian methods gave generally wider interval estimates. However, the point estimates for the individual studies were substantially different, particularly for small studies. CONCLUSIONS: For the data sets considered, Bayesian methods, which are computer intensive but intuitively appealing, provided results that were consistent with the classic fixed-effect Peto method. Introduction of the more extreme data points did not alter this conclusion.


1992 ◽  
Vol 11 (10) ◽  
pp. 1377-1389 ◽  
Author(s):  
Constantine Gatsonis ◽  
Joel B. Greenhouse

2017 ◽  
Vol 27 (10) ◽  
pp. 3167-3182 ◽  
Author(s):  
Joost van Rosmalen ◽  
David Dejardin ◽  
Yvette van Norden ◽  
Bob Löwenberg ◽  
Emmanuel Lesaffre

Data of previous trials with a similar setting are often available in the analysis of clinical trials. Several Bayesian methods have been proposed for including historical data as prior information in the analysis of the current trial, such as the (modified) power prior, the (robust) meta-analytic-predictive prior, the commensurate prior and methods proposed by Pocock and Murray et al. We compared these methods and illustrated their use in a practical setting, including an assessment of the comparability of the current and the historical data. The motivating data set consists of randomised controlled trials for acute myeloid leukaemia. A simulation study was used to compare the methods in terms of bias, precision, power and type I error rate. Methods that estimate parameters for the between-trial heterogeneity generally offer the best trade-off of power, precision and type I error, with the meta-analytic-predictive prior being the most promising method. The results show that it can be feasible to include historical data in the analysis of clinical trials, if an appropriate method is used to estimate the heterogeneity between trials, and the historical data satisfy criteria for comparability.


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