doi:http://dx.doi.org/10.5705/ss.2010.228
Abstract: Existing methods for analysis of survival data arising from cohort sampling are largely based on Cox's model and pertained to a certain type of sampling design. This paper applies the general linear transformation model, which includes Cox's model and proportional odds model as special cases, to a class of sampling designs including nested case-control, case-cohort and classical case-control designs. A simple likelihood-based method is developed, and the resulting estimator of the regression coefficient is shown to be consistent and asymptotic normal. The computation and inference procedures are straightforward. In addition to the simplicity and generality of the method, it also has minimal loss of efficiency as the observations with missing covariates that are not used contain little information about the regression parameter. The proposed estimation performs well in simulation studies and is applied to analyze the Colorado Plateau uranium miners cohort data.
Key words and phrases: Complete case analysis, generalized case-cohort sampling, linear transformation model, missing at random.