Distribution-free inference on contrasts of arbitrary summary measures of survival.

Abstract:

:We present an approach for inference on contrasts of clinically meaningful functionals of a survivor distribution (e.g., restricted mean, quantiles) that can avoid strong parametric or semiparametric assumptions on the underlying structure of the data. In this multistage approach, we first use an adaptive predictive model to estimate conditional survival distributions based on covariates. We then estimate nonparametrically one or more functionals of survival from the covariate-specific survival curves and evaluated contrasts of those functionals. We find that the use of an adaptive nonparametric tree-based predictive model leads to minimal loss in precision when semiparametric assumptions hold and provides marked improvement in accuracy when those assumptions are invalid. Therefore, this work as a whole supports the use of survival summaries appropriate to a given medical application, whether that be, for example, the median or 75th percentile in some settings or perhaps a restricted mean in others. The approach is also compared with the Mayo R score for primary biliary cirrhosis prognosis.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Rudser KD,LeBlanc ML,Emerson SS

doi

10.1002/sim.4505

subject

Has Abstract

pub_date

2012-07-20 00:00:00

pages

1722-37

issue

16

eissn

0277-6715

issn

1097-0258

journal_volume

31

pub_type

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