Bayesian Sensitivity Analysis for a Missing Data Model

Incorporating Covariates via a Cox Model

Master thesis (2023)

Authors

C. van Vliet Electrical Engineering, Mathematics and Computer Science

Contributors

A.W. van der Vaart Statistics - EEMCS (mentor)
J.H. Krijthe Pattern Recognition and Bioinformatics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Christian van Vliet
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Published Date

02-08-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In problems with missing data, the data are often considered to be missing at random. This assumption can not be checked from the data. We need to assess the sensitivity of study conclusions to violations of non-identifiable assumptions. This thesis performs Bayesian sensitivity analysis for a missing data model with life time outcomes and covariate information. The outcome distribution is modelled through a Cox model, with a beta process prior on the cumulative hazard function. We run experiments in a simulation study to test the performance of the model in scenarios with simulated data of several sample sizes. We show the validity of the model in the context of Bayesian sensitivity analysis, and propose extensions.