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A Bayesian chi-squared goodness-of-fit test for censored data models
Jing Cao1, Ann Moosman, Valen E Johnson
1Department of Statistical Science, Southern Methodist University, Dallas, Texas 75275, USA. jcao@smu.edu
Biometrics
|August 4, 2009
Summary
This study introduces a new Bayesian chi-squared diagnostic for censored data analysis. It offers improved power for detecting model departures, especially under heavy censoring, using only observed failure times.
Area of Science:
- Statistics
- Biostatistics
- Survival Analysis
Background:
- Censored data analysis is crucial in many fields, but traditional methods struggle with missing failure times.
- Existing model diagnostics may lack power or applicability across diverse censored data models.
Purpose of the Study:
- To propose a novel Bayesian chi-squared model diagnostic for censored data.
- To evaluate its performance against existing methods, particularly under heavy censoring.
Main Methods:
- Developed a chi-squared test statistic based on observed failure times from Markov chain Monte Carlo output.
- Conducted simulation studies to compare the new diagnostic with a standard alternative (Akritas, 1988).
Main Results:
- The proposed diagnostic demonstrated higher power in detecting model departures under heavy censoring compared to complete data tests.
- Simulation results showed comparable power and better Type I error rates than the Akritas test.
Conclusions:
- The Bayesian chi-squared diagnostic is a powerful and flexible tool for analyzing censored data.
- Its ability to use only observed failure times makes it advantageous, especially when censoring is prevalent.
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