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Pairwise residuals and diagnostic tests for misspecified dependence structures in models for binary longitudinal data
Nina Breinegaard1, Sophia Rabe-Hesketh2, Anders Skrondal3
1Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark.
Statistics in Medicine
|October 31, 2017
Summary
New diagnostic tests improve the analysis of binary longitudinal data by detecting misspecified dependence structures. These methods use pairwise concordance residuals for robust model checking.
Area of Science:
- Statistics
- Biostatistics
- Longitudinal Data Analysis
Background:
- Maximum likelihood estimation for binary longitudinal data can be inconsistent if the dependence structure is misspecified.
- Existing diagnostic tools for detecting such misspecifications are limited.
Purpose of the Study:
- To develop novel residuals and diagnostic tests for identifying misspecified dependence structures in binary longitudinal data models.
- To provide robust methods for model checking in the presence of complex covariate patterns.
Main Methods:
- Developed residuals and diagnostic tests by comparing observed and expected frequencies of response patterns over time.
- Utilized lower-order marginal tables, specifically two-way tables aggregated over covariate patterns, to address data sparseness.
- Proposed pairwise concordance residuals for exploratory diagnostics and constructing generic and targeted dependence structure tests.
Main Results:
- The proposed pairwise concordance residuals effectively detect misspecified dependence structures.
- The methods are adaptable for generic tests of overall dependence and targeted tests for excess serial dependence.
- The diagnostic tests are straightforward to implement and perform well across various scenarios.
Conclusions:
- The developed methods offer valuable tools for assessing the adequacy of dependence structures in binary longitudinal data models.
- These diagnostics are broadly applicable, irrespective of the number of time points or the complexity of covariates.
- The pairwise concordance approach enhances the reliability of statistical modeling for longitudinal binary outcomes.
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