Interpreting Treatment Effects Using Posterior Probabilities: A Bayesian Reanalysis of 230 Phase III Oncology Trials.

Alexander D Sherry^1,2, Pavlos Msaouel^3,4, Gabrielle S Kupferman¹, Timothy A Lin¹, Joseph Abi Jaoude⁵, Ramez Kouzy¹, Molly B El-Alam¹, Roshal Patel⁶, Alex Koong¹, Christine Lin¹, Adina H Passy¹, Avital M Miller¹, Esther J Beck¹, Clifton David Fuller¹, Tomer Meirson⁷, Zachary R McCaw^8,9, Ethan B Ludmir^10,11

¹Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.

²Department of Radiation Oncology, Mayo Clinic, Rochester, MN.

³Division of Cancer Medicine, Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.

⁴Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX.

⁵Department of Radiation Oncology, Stanford University, Stanford, CA.

⁶Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY.

⁷Davidoff Cancer Center, Rabin Medical Center-Beilinson Hospital, Petach Tikva, Israel.

⁸Insitro, South San Francisco, CA.

⁹Department of Biomedical Informatics, University of North Carolina at Chapel Hill, Chapel Hill, NC.

¹⁰Division of Radiation Oncology, Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.

¹¹Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX.

PubMed DOI

Abstract

PURPOSE: Most oncology trials define superiority according to dichotomized P value thresholds, which are frequently misinterpreted. Posterior probability, however, directly estimates the probability of the hypothesis at hand. Here, we reanalyze a large collection of modern phase III trials and benchmark posterior probability versus the standard trial interpretation based on statistical significanc…

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