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

:::info Authors:

(1) Yueqi Shen, Department of Biostatistics, University of North Carolina at Chapel Hill ([email protected]);

(2) Matthew A. Psioda, GSK;

(3) Joseph G. Ibrahim, Department of Biostatistics, University of North Carolina at Chapel Hill.

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Table of Links

Abstract and 1 Introduction: BayesPPDSurv

2 Theoretical Framework

2.1 The Power Prior and the Normalized Power Prior

2.2 The Piecewise Constant Hazard Proportional Hazards (PWCH-PH) Model

2.3 Power Prior for the PWCH-PH Model

2.4 Implementing the Normalized Power Prior for the PWCH-PH Model

2.5 Bayesian Sample Size Determination

2.6 Data Simulation for the PWCH-PH Model

3 Using BayesPPDSurv

3.1 Sampling Priors

4 Case Study: Melanoma Clinical Trial Design

5 Discussion and References

2 Theoretical Framework 2.1 The Power Prior and the Normalized Power Prior

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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). \n

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2.3 Power Prior for the PWCH-PH Model

\ \n 2.4 Implementing the Normalized Power Prior for the PWCH-PH Model

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2.5 Bayesian Sample Size Determination

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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:

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\ \ 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:

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:::info This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.

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