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Likelihood-based Inferential Methods Testing Effect Size Measures for Stratified Correlated Binary Data with Bilateral Outcomes
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Likelihood-based Inferential Methods Testing Effect Size Measures for Stratified Correlated Binary Data with Bilateral Outcomes
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Dissertation
Likelihood-based Inferential Methods Testing Effect Size Measures for Stratified Correlated Binary Data with Bilateral Outcomes
2020
Overview
Binary data with bilateral outcomes is often encountered in medical comparative clinical studies where patients receive treatment on paired organs or body parts. Usually, one would expect the measurement contributed from the paired organs of the single person to be correlated. Methods of analysis that overlooked this feature of intraclass correlation may lead to an invalid inference. Taking this issue into consideration, we developed a series of likelihood-based test statistics for proportion-related effect measures on stratified correlated binary data with bilateral outcomes, including (1) three homogeneity test statistics for proportion ratios across strata based on Likelihood ratio test, Score test, and Wald-type test, (2) four confidence interval estimators (unconstrained and semi-constrained MLE-based weighted Wald-type confidence interval, profile likelihood confidence interval, and constrained MLE-based Score confidence interval) for proportion ratios, and (3) three test statistics testing the homogeneity of odds ratios. Two types of Monte Carlo simulation studies were designed and conducted to evaluate the performance of the proposed testing methods and confidence interval estimators. The presented testing procedures were demonstrated by revisiting the illustrative rheumatology study.
