Simultaneous modelling of operative mortality and long-term survival aftercoronary artery bypass surgery

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 propor tional hazards or fully parametric, to investigate the relationship between the response and covariates. In certain circumstances it may be more natur al to view the distribution of the response variable as consisting of two o r more parts since the survival curve appears segmented. This article addre sses such a scenario and we propose a model for simultaneously investigatin g the effects of covariates over the two segments. The model is an analogue of that proposed by Lambert for zero-inflated Poisson regression. The appl ication 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 di splays 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 a rtery bypass surgery illustrates the usefulness of the proposed model. Copy right (C) 2001 John Wiley & Sons, Ltd.