Adaptive regression splines in the Cox model.
We develop a method for constructing adaptive regression spline models for the exploration of survival data. The method combines Cox's (1972, Journal of the Royal Statistical Society, Series B 34, 187-200) regression model with a weighted least-squares version of the multivariate adaptive regressi on spline (MARS) technique of Friedman (1991, Annals of Statistics 19, 1-141) to adaptively select the knots and covariates. The new technique can automatically fit models with terms that represent nonlinear effects and interactions among covariates. Applications based on simulated data and data from a clinical trial for myeloma are presented. Results from the myeloma application identified several important prognostic variables, including a possible nonmonotone relationship with survival in one laboratory variable. Results are compared to those from the adaptive hazard regression (HARE) method of Kooperberg, Stone, and Truong (1995, Journal of the American Statistical Association 90, 78-94).
LeBlanc
M
M
Fred Hutchinson Cancer Research Center, Seattle, Washington 98104, USA. mikel@swog.fhcrc.org
Crowley
J
J
D001699:Biometry; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D008297:Male; D015233:Models, Statistical; D009101:Multiple Myeloma / Q000097:blood / Q000188:drug therapy / Q000401:mortality; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D012044:Regression Analysis; D016019:Survival Analysis