Authors:
(1) Yueqi Shen, Department of Biostatistics, University of North Carolina at Chapel Hill (ys137@live.unc.edu);
(2) Matthew A. Psioda, GSK;
(3) Joseph G. Ibrahim, Department of Biostatistics, University of North Carolina at Chapel Hill. Authors: Authors: (1) Yueqi Shen, Department of Biostatistics, University of North Carolina at Chapel Hill (ys137@live.unc.edu); (2) Matthew A. Psioda, GSK; (3) Joseph G. Ibrahim, Department of Biostatistics, University of North Carolina at Chapel Hill. Table of Links Abstract and 1 Introduction: BayesPPDSurv Abstract and 1 Introduction: BayesPPDSurv 2 Theoretical Framework 2.1 The Power Prior and the Normalized Power Prior 2.1 The Power Prior and the Normalized Power Prior 2.2 The Piecewise Constant Hazard Proportional Hazards (PWCH-PH) Model 2.2 The Piecewise Constant Hazard Proportional Hazards (PWCH-PH) Model 2.3 Power Prior for the PWCH-PH Model 2.3 Power Prior for the PWCH-PH Model 2.4 Implementing the Normalized Power Prior for the PWCH-PH Model 2.4 Implementing the Normalized Power Prior for the PWCH-PH Model 2.5 Bayesian Sample Size Determination 2.5 Bayesian Sample Size Determination 2.6 Data Simulation for the PWCH-PH Model 2.6 Data Simulation for the PWCH-PH Model 3 Using BayesPPDSurv 3 Using BayesPPDSurv 3.1 Sampling Priors 3.1 Sampling Priors 4 Case Study: Melanoma Clinical Trial Design 4 Case Study: Melanoma Clinical Trial Design 5 Discussion and References 5 Discussion and References 2 Theoretical Framework 2.1 The Power Prior and the Normalized Power Prior 2.2 The Piecewise Constant Hazard Proportional Hazards (PWCH-PH) Model In BayesPPDSurv, we implement the stratified proportional hazards model with piecewise constant baseline hazard within each stratum, which is a common approach for Bayesian analysis of time-to-event data (Ibrahim et al., 2001). BayesPPDSurv 2.3 Power Prior for the PWCH-PH Model 2.4 Implementing the Normalized Power Prior for the PWCH-PH Model 2.5 Bayesian Sample Size Determination 2.6 Data Simulation for the PWCH-PH Model Following Psioda et al. (2018), we describe the steps for simulating the observed data for the PWCHPH model. We simulate the complete data for subject i through the following procedure: i The above procedure yields a hypothetical complete dataset corresponding to a scenario where all subjects are followed until the event is observed or they drop out. One constructs the observed dataset from the complete dataset as follows: This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv

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How BayesPPDSurv Brings Cutting-Edge Bayesian Survival Modeling to the Masses

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