Structured fusion lasso penalized multi-state models

Abstract

Multi‐state models generalize survival or duration time analysis to the estimation of transition‐specific hazard rate functions for multiple transitions. When each of the transition‐specific risk functions is parametrized with several distinct covariate effect coefficients, this leads to a model of potentially high dimension. To decrease the parameter space dimensionality and to work out a clear image of the underlying multi‐state model structure, one can either aim at setting some coefficients to zero or to make coefficients for the same covariate but two different transitions equal. The first issue can be approached by penalizing the absolute values of the covariate coefficients as in lasso regularization. If, instead, absolute differences between coefficients of the same covariate on different transitions are penalized, this leads to sparse competing risk relations within a multi‐state model, that is, equality of covariate effect coefficients. In this paper, a new estimation approach providing sparse multi‐state modelling by the aforementioned principles is established, based on the estimation of multi‐state models and a simultaneous penalization of the L1‐norm of covariate coefficients and their differences in a structured way. The new multi‐state modelling approach is illustrated on peritoneal dialysis study data and implemented in the R package penMSM.