Using threshold regression to analyze survival data from complex surveys: With application to mortality linked NHANES III Phase II genetic data

• Published in: Statistics in medicine Vol. 37; no. 7; pp. 1162 - 1177
• Main Authors: Li, Yan, Xiao, Tao, Liao, Dandan, Lee, Mei‐Ling Ting
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 30.03.2018
• Subjects: Cluster Analysis; Computer Simulation; cox proportional hazard; cure rate; Databases, Genetic; Genetics; Humans; intracluster correlation; Likelihood Functions; Medical statistics; Mortality; Multivariate Analysis; Nutrition Surveys; pseudo‐maximum likelihood estimation; Regression Analysis; stratified multistage sampling; Survival Analysis; cox proportional hazard; intracluster correlation; stratified multistage sampling; cure rate; pseudo-maximum likelihood estimation
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.7575

The Cox proportional hazards (PH) model is a common statistical technique used for analyzing time‐to‐event data. The assumption of PH, however, is not always appropriate in real applications. In cases where the assumption is not tenable, threshold regression (TR) and other survival methods, which do not require the PH assumption, are available and widely used. These alternative methods generally assume that the study data constitute simple random samples. In particular, TR has not been studied in the setting of complex surveys that involve (1) differential selection probabilities of study subjects and (2) intracluster correlations induced by multistage cluster sampling. In this paper, we extend TR procedures to account for complex sampling designs. The pseudo‐maximum likelihood estimation technique is applied to estimate the TR model parameters. Computationally efficient Taylor linearization variance estimators that consider both the intracluster correlation and the differential selection probabilities are developed. The proposed methods are evaluated by using simulation experiments with various complex designs and illustrated empirically by using mortality‐linked Third National Health and Nutrition Examination Survey Phase II genetic data.