A special case of reduced rank models for identification and modelling of time varying effects in survival analysis.

Abstract:

:Flexible survival models are in need when modelling data from long term follow-up studies. In many cases, the assumption of proportionality imposed by a Cox model will not be valid. Instead, a model that can identify time varying effects of fixed covariates can be used. Although there are several approaches that deal with this problem, it is not always straightforward how to choose which covariates should be modelled having time varying effects and which not. At the same time, it is up to the researcher to define appropriate time functions that describe the dynamic pattern of the effects. In this work, we suggest a model that can deal with both fixed and time varying effects and uses simple hypotheses tests to distinguish which covariates do have dynamic effects. The model is an extension of the parsimonious reduced rank model of rank 1. As such, the number of parameters is kept low, and thus, a flexible set of time functions, such as b-splines, can be used. The basic theory is illustrated along with an efficient fitting algorithm. The proposed method is applied to a dataset of breast cancer patients and compared with a multivariate fractional polynomials approach for modelling time-varying effects. Copyright © 2016 John Wiley & Sons, Ltd.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Perperoglou A

doi

10.1002/sim.7088

subject

Has Abstract

pub_date

2016-12-10 00:00:00

pages

5135-5148

issue

28

eissn

0277-6715

issn

1097-0258

journal_volume

35

pub_type

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