2013Journal of Biopharmaceutical StatisticsRequires access

Estimation of the Common Risk Difference in Stratified Paired Binary Data with Homogeneous Stratum Effect

Yan D. Zhao, Dewi Rahardja

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Abstract

McNemar's test is commonly used to test for the risk difference between two binary variables on matched pairs. For stratified paired binary data, recently a test for the homogeneous stratum effect (HSE) has been developed. If HSE is rejected, then McNemar's test should be applied by stratum; otherwise, in this article we propose a concept of common risk difference (CRD) across the strata and derive point estimators and confidence intervals for CRD. We use a cancer study for illustration and conduct simulations to recommend point estimators and associated confidence intervals with good statistical properties.

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What this paper is about

McNemar's test is commonly used to test for the risk difference between two binary variables on matched pairs. For stratified paired binary data, recently a test for the homogeneous stratum effect (HSE) has been developed. If HSE is rejected, then McNemar's test should be applied by stratum; otherwise, in this article we propose a concept of common risk difference (CRD) across the strata and derive point estimators and confidence intervals for CRD. We use a cancer study for illustration and conduct simulations to recommend point estimators and associated confidence intervals with good statistical properties.

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Available abstract

McNemar's test is commonly used to test for the risk difference between two binary variables on matched pairs. For stratified paired binary data, recently a test for the homogeneous stratum effect (HSE) has been developed. If HSE is rejected, then McNemar's test should be applied by stratum; otherwise, in this article we propose a concept of common risk difference (CRD) across the strata and derive point estimators and confidence intervals for CRD. We use a cancer study for illustration and conduct simulations to recommend point estimators and associated confidence intervals with good statistical properties.

Key concepts: Stratum, Statistics, Homogeneous, Mathematics, Binary number, Binary data, Stratified sampling, Econometrics

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