Survival Data Analysis with Time-Dependent Covariates Using Generalized Additive Models | Zendy

Masaaki Tsujitani | Zendy; Yusuke Tanaka | Zendy; Masato Sakon | Zendy

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Survival Data Analysis with Time-Dependent Covariates Using Generalized Additive Models

Author(s) -

Masaaki Tsujitani,

Yusuke Tanaka,

Masato Sakon

Publication year - 2012

Publication title -

computational and mathematical methods in medicine

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.462

H-Index - 48

eISSN - 1748-6718

pISSN - 1748-670X

DOI - 10.1155/2012/986176

Subject(s) - covariate , proportional hazards model , survival analysis , smoothing , generalized additive model , statistics , survival function , accelerated failure time model , mathematics , smoothing spline , medicine , computer science , bilinear interpolation , spline interpolation

We discuss a flexible method for modeling survival data using penalized smoothing splines when the values of covariates change for the duration of the study. The Cox proportional hazards model has been widely used for the analysis of treatment and prognostic effects with censored survival data. However, a number of theoretical problems with respect to the baseline survival function remain unsolved. We use the generalized additive models (GAMs) with B splines to estimate the survival function and select the optimum smoothing parameters based on a variant multifold cross-validation (CV) method. The methods are compared with the generalized cross-validation (GCV) method using data from a long-term study of patients with primary biliary cirrhosis (PBC).

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