Weighted likelihood, pseudo-likelihood and maximum likelihood methods for logistic regression analysis of two-stage data.
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.
Breslow
N E
NE
Department of Biostatistics, University of Washington, Seattle 98195-7232, USA.
Holubkov
R
R
D000328:Adult; D017677:Age Distribution; D000368:Aged; D016022:Case-Control Studies; D003625:Data Collection; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007223:Infant; D007226:Infant Mortality; D007231:Infant, Newborn; D016013:Likelihood Functions; D016015:Logistic Models; D008175:Lung Neoplasms / Q000209:etiology; D008297:Male; D008875:Middle Aged; D009784:Occupational Diseases / Q000209:etiology; D012307:Risk Factors; D012494:Sampling Studies; D017678:Sex Distribution; D012907:Smoking / Q000009:adverse effects; D016019:Survival Analysis