Simultaneous modelling of operative mortality and long-term survival after coronary artery bypass surgery.

Abstract:

:Typical analyses of lifetime data treat the time to death or failure as the response variable and use a variety of modelling strategies such as proportional hazards or fully parametric, to investigate the relationship between the response and covariates. In certain circumstances it may be more natural to view the distribution of the response variable as consisting of two or more parts since the survival curve appears segmented. This article addresses such a scenario and we propose a model for simultaneously investigating the effects of covariates over the two segments. The model is an analogue of that proposed by Lambert for zero-inflated Poisson regression. The application is central to the model development and is concerned with survival after coronary artery bypass surgery. Here operative mortality, defined as death within 30 days after surgery, and long-term mortality, are viewed as distinct outcomes. For the application considered, the survivor function displays much steeper descent during the first 30 days after surgery, that is, for operative mortality, than after this period. An investigation of the effects of covariates on operative and long-term mortality after coronary artery bypass surgery illustrates the usefulness of the proposed model.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Ghahramani M,Dean CB,Spinelli JJ

doi

10.1002/sim.822

subject

Has Abstract

pub_date

2001-07-15 00:00:00

pages

1931-45

issue

13

eissn

0277-6715

issn

1097-0258

journal_volume

20

pub_type

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