WEIGHTED LIKELIHOOD, PSEUDO-LIKELIHOOD AND MAXIMUM LIKELIHOOD METHODS FOR LOGISTIC REGRESSION ANALYSIS OF TWO-STAGE DATA

✍ Scribed by NORMAN E. BRESLOW; RICHARD HOLUBKOV

• Publisher: John Wiley and Sons
• Year: 1997
• Tongue: English
• Weight: 316 KB
• Volume: 16
• Category: Article
• ISSN: 0277-6715
• DOI: 10.1002/(sici)1097-0258(19970115)16:1<103::aid-sim474>3.0.co;2-p

✦ Synopsis

General approaches to the fitting of binary response models to data collected in two-stage and other stratified sampling designs include weighted likelihood, pseudo-likelihood and full maximum likelihood. In previous work the authors developed the large sample theory and methodology for fitting of logistic regression models to two-stage case-control data using full maximum likelihood. The present paper describes computational algorithms that permit efficient estimation of regression coefficients using weighted, pseudo-and full maximum likelihood. It also presents results of a simulation study involving continuous covariables where maximum likelihood clearly outperformed the other two methods and discusses the analysis of data from three bona fide case-control studies that illustrate some important relationships among the three methods. A concluding section discusses the application of two-stage methods to case-control studies with validation subsampling for control of measurement error.