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Activity Number: 372 - SPEED: SPAAC SESSION IV
Type: Topic-Contributed
Date/Time: Thursday, August 12, 2021 : 12:00 PM to 1:50 PM
Sponsor: Biometrics Section
Abstract #318382
Title: Interval Estimation of Relative Risks for Combined Unilateral and Bilateral Correlated Data under the Assumption of Equal Dependence
Author(s): Kejia Wang* and Chang-Xing Ma
Companies: State University of New York at Buffalo and State University of New York at Buffalo
Keywords: Bilateral Correlated Data ; Unilateral data; Relative Risk; Intraclass correlation ; Score confidence interval; Rosner's constant R model
Abstract:

Measurements are generally collected as unilateral or bilateral data in clinical trials or observational studies. For example, in ophthalmology studies, the primary outcome is often obtained from one eye or both eyes of an individual. Relative Risk is usually the parameter of interest and is commonly used in medical studies. We develop three confidence intervals for the Relative Risk for combined unilateral and bilateral correlated data. The proposed confidence intervals are based on maximum likelihood estimates of parameters derived using the Fisher scoring method. Simulation studies are conducted to evaluate the performance of proposed confidence intervals with respect to the coverage probability, the average interval width, and the degree and symmetry of coverage. We also compare the proposed methods with the method of variance estimates recovery (MOVER) based confidence interval and the confidence interval obtained from the modified Poisson model for correlated data. We find that the proposed score confidence interval method performs better in terms of the coverage probability and the average width. We illustrate the methods with a real-world example.


Authors who are presenting talks have a * after their name.

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