Bayesian methods in clinical trials: a Bayesian analysis of ECOG trials E1684 and E1690.

TitleBayesian methods in clinical trials: a Bayesian analysis of ECOG trials E1684 and E1690.
Publication TypeJournal Article
Year of Publication2012
AuthorsIbrahim, Joseph G., Ming-Hui Chen, and Haitao Chu
JournalBMC Med Res Methodol
Date Published2012 Nov 29
KeywordsBayes Theorem, Clinical Trials as Topic, Disease-Free Survival, Humans, Interferon-alpha, Melanoma, Multivariate Analysis, Neoplasm Recurrence, Local, Proportional Hazards Models, Reproducibility of Results, Skin Neoplasms, Statistical Distributions, Survival Analysis, Treatment Outcome

BACKGROUND: E1684 was the pivotal adjuvant melanoma trial for establishment of high-dose interferon (IFN) as effective therapy of high-risk melanoma patients. E1690 was an intriguing effort to corroborate E1684, and the differences between the outcomes of these trials have embroiled the field in controversy over the past several years. The analyses of E1684 and E1690 were carried out separately when the results were published, and there were no further analyses trying to perform a single analysis of the combined trials.METHOD: In this paper, we consider such a joint analysis by carrying out a Bayesian analysis of these two trials, thus providing us with a consistent and coherent methodology for combining the results from these two trials.RESULTS: The Bayesian analysis using power priors provided a more coherent flexible and potentially more accurate analysis than a separate analysis of these data or a frequentist analysis of these data. The methodology provides a consistent framework for carrying out a single unified analysis by combining data from two or more studies.CONCLUSIONS: Such Bayesian analyses can be crucial in situations where the results from two theoretically identical trials yield somewhat conflicting or inconsistent results.

Alternate JournalBMC Med Res Methodol
Original PublicationBayesian methods in clinical trials: A Bayesian analysis of ECOG trials E1684 and E1690.
PubMed ID23194570
PubMed Central IDPMC3571975
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
CA 74015 / CA / NCI NIH HHS / United States
GM 70335 / GM / NIGMS NIH HHS / United States