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Published on: December 10, 2012
Impact of nonignorable coarsening on Bayesian inference
Jiameng Zhang1, Daniel F Heitjan
1Genentech, Inc., South San Francisco, CA 94080, USA. zhang.jiameng@gene.com
Biostatistics (Oxford, England)
|January 12, 2007
Summary
This study introduces a Bayesian approach to measure sensitivity to nonignorable data coarsening. The new method, adapted from the index of local sensitivity to nonignorability (ISNI), assesses the robustness of statistical inferences.
Area of Science:
- Statistics
- Biostatistics
- Data Science
Background:
- The coarse data model addresses various data incompleteness forms like censoring and grouping.
- Nonignorable coarsening occurs when data incompleteness depends on unobserved outcomes, potentially biasing inferences.
- Sensitivity analysis is crucial for evaluating the impact of nonignorability on statistical models.
Purpose of the Study:
- To adapt the Index of Local Sensitivity to Nonignorability (ISNI) for Bayesian analyses.
- To propose a method for assessing the robustness of Bayesian inferences to nonignorable data coarsening.
- To demonstrate the application of the adapted ISNI in real-world biostatistical examples.
Main Methods:
- Generalized the coarse data model to incorporate Bayesian inference.
- Defined a Bayesian version of ISNI as the derivative of the posterior expectation.
- Derived formulas for various statistical models.
- Applied the method to analyze missing CD4 counts and censored relapse times.
Main Results:
- The adapted ISNI provides a quantitative measure of sensitivity to nonignorable coarsening in Bayesian models.
- The method was successfully applied to two distinct biostatistical datasets, demonstrating its practical utility.
- Results highlight the importance of accounting for nonignorability in specific clinical trial settings.
Conclusions:
- The proposed Bayesian ISNI is a valuable tool for assessing the robustness of analyses to nonignorable coarsening.
- This approach enhances the reliability of statistical inferences in the presence of complex data incompleteness.
- Further research can extend this methodology to a wider range of statistical models and data types.
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