Originally published as JCO Early Release 10.1200/JCO.2008.16.2586 on March 23 2009

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Interpreting Trial Results in Light of Conflicting Evidence: A Bayesian Analysis of Adjuvant Chemotherapy for Non–Small-Cell Lung Cancer

From the Department of Medicine, Division of Hematology and Oncology, Beth Israel Deaconess Hospital, Harvard Medical School; Institute for Technology Assessment, and Department of Medicine, Division of Hematology and Oncology, Massachusetts General Hospital, Boston, MA; and Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY.

Corresponding author: Rebecca A. Miksad, MD, MPH, Beth Israel Deaconess Hospital/Harvard University, 330 Brookline Ave, Shapiro 9, Boston, MA 02215; e-mail: rmiksad{at}bidmc.harvard.edu.

Purpose When successive randomized trials contradict prior evidence, clinicians may be unsure how to evaluate them: Does accumulating evidence warrant changing practice? An increasingly popular solution, Bayesian statistics quantitatively evaluate new results in context. This study provides a clinically relevant example of Bayesian methods.

Methods Three recent non–small-cell lung cancer adjuvant chemotherapy trials were evaluated in light of prior conflicting data. Results were used from International Adjuvant Lung Trial (IALT), JBR.10, and Adjuvant Navelbine International Trialist Association (ANITA). Prior evidence was sequentially updated to calculate the probability of each survival benefit level (overall and by stage) and variance. Sensitivity analysis was performed using expert opinion and uninformed estimates of survival benefit prior probability.

Results The probability of a 4% survival benefit increased from 33% before IALT to 64% after IALT. After sequential updating with JBR.10 and ANITA, this probability was 82% (hazard ratio = 0.84; 95% CI, 0.77 to 0.91). IALT produced the largest decrease in variance (61%) and decreased the chance of survival decrement to 0%. Sensitivity analysis did not support a survival benefit after IALT. However, sequential updating substantiated a 4% survival benefit and, for stage II and III, more than 90% probability of a 6% benefit and 50% probability of a 12% benefit.

Conclusion When evaluated in context with prior data, IALT did not support a 4% survival benefit. However, sequential updating with JBR.10 and ANITA did. A model for future assessments, this study demonstrates the unique ability of Bayesian analysis to evaluate results that contradict prior evidence.

Supported in part by National Cancer Institute Grant No. R25T CA 92203 (R.A.M. and T.G.R.). T.G.R. was also supported in part by an unrestricted Health Outcomes Starter grant from the PhRMA Foundation.

Presented in part at the 28th Annual Meeting of the Society of Medical Decision Making Annual Meeting, October 15-18, 2006, Boston, MA; and at the 40th Annual Meeting of the American Society of Clinical Oncology, June 5-8, 2004, New Orleans, LA.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

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