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A semi-parametric threshold regression analysis of sexually transmitted infections in adolescent women.
Yu Z; Tu W; Lee M-LT
Stat Med 2009 Oct; 28(24):3029-3042
Time-to-event analysis of sexually transmitted infection data is often complicated by the existence of nonproportional hazards and nonlinear independent variable effects. Methods without the proportional hazards assumption, such as threshold regression models, have been successfully used in many applications. This paper seeks to extend the existing threshold regression models to accommodate the nonlinear independent variable effects. Specifically, we incorporated penalized and regression splines to the threshold regression models for added modeling flexibility. Cross validation methods were used for the selection of the number of knots and for the determination of smoothing parameters. Variance estimates were proposed for inference purposes. Simulation results showed that the proposed methods were able to achieve nonparametric function and parametric coefficient estimates that are close to their true values. Simulation also demonstrated satisfactory performance of variance estimates. Using the proposed methods, we analyzed time from sexual debut to the first infection with Chlamydia trachomatis infection in a group of young women. Analysis shows that the lifetime number of sexual partners has a nonlinear effect on the risk of C. trachomatis infection and the infection risks were differential by ethnicity and age of sexual debut.
Age-factors; Analytical-methods; Analytical-processes; Exposure-assessment; Exposure-levels; Exposure-methods; Mathematical-models; Racial-factors; Risk-analysis; Sexually-transmitted-diseases; Statistical-analysis; Author Keywords: first hitting time model; penalized spline; regression spline; cross validation; survival analysis; nonproportional hazard
Zhangsheng Yu , Division of Biostatistics, Indiana University School of Medicine, 410 W 10th St., Indianapolis, IN 46254 USA
Issue of Publication
Transportation, Warehousing and Utilities
Statistics in Medicine
University of Maryland - College Park
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