Fitting general relative risk models for survival time and matched case-control analysis.
Cox proportional hazards regression analysis of survival data and conditional logistic regression analysis of matched case-control data are methods that are widely used by epidemiologists. Standard statistical software packages accommodate only log-linear model forms, which imply exponential exposure-response functions and multiplicative interactions. In this paper, the authors describe methods for fitting non-log-linear Cox and conditional logistic regression models. The authors use data from a study of lung cancer mortality among Colorado Plateau uranium miners (1950-1982) to illustrate these methods for fitting general relative risk models to matched case-control control data, countermatched data with weights, d:m matching, and full cohort Cox regression using the SAS statistical package (SAS Institute Inc., Cary, North Carolina).
Langholz
Bryan
B
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 1540 Alcazar Street, CHP-220, Los Angeles, CA 90033, USA. langholz@usc.edu
Richardson
David B
DB
D000395:Air Pollutants, Occupational; D016022:Case-Control Studies; D003120:Colorado / Q000453:epidemiology; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000401:mortality; D008906:Mining; D016273:Occupational Exposure; D016017:Odds Ratio; D016016:Proportional Hazards Models; D016019:Survival Analysis; D014501:Uranium
D000395:Air Pollutants, Occupational; D014501:Uranium