Fast QLB algorithm and hypothesis tests in logistic model for ophthalmologic bilateral correlated data
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AbstractIn ophthalmologic or otolaryngologic studies, bilateral correlated data often arise when observations involving paired organs (e.g., eyes, ears) are measured from each subject. Based on Donner's model , in this paper, we focus on investigating the relationship between the disease probability and covariates (such as ages, weights, gender, and so on) via the logistic regression for the analysis of bilateral correlated data. We first propose a new minorization–maximization (MM) algorithm and a fast quadratic lower bound (QLB) algorithm to calculate the maximum likelihood estimates of the vector of regression coefficients, and then develop three large-sample tests (i.e., the likelihood ratio test, Wald test, and score test) to test if covariates have a significant impact on the disease probability. Simulation studies are conducted to evaluate the performance of the proposed fast QLB algorithm and three testing methods. A real ophthalmologic data set in Iran is used to illustrate the proposed methods.
Acceptance Date24/07/2020
All Author(s) ListYi-Qi Lin, Yu-Shun Zhang, Guo-Liang Tian, Chang-Xing Ma
Journal nameJournal of Biopharmaceutical Statistics
Volume Number31
Issue Number1
PublisherTaylor & Francis
Place of PublicationUK
Pages91 - 107
LanguagesEnglish-United States
KeywordsAssembly and decomposition technique, bilateral correlated data, fast QLB algorithm, logistic regression model, MM algorithm, ophthalmologic study

Last updated on 2022-15-01 at 00:13