Survival analysis for recurrent event data: an application to childhood infectious diseases
✍ Scribed by Patrick J. Kelly; Lynette L-Y. Lim
- Publisher
- John Wiley and Sons
- Year
- 2000
- Tongue
- English
- Weight
- 152 KB
- Volume
- 19
- Category
- Article
- ISSN
- 0277-6715
No coin nor oath required. For personal study only.
✦ Synopsis
Many extensions of survival models based on the Cox proportional hazards approach have been proposed to handle clustered or multiple event data. Of particular note are "ve Cox-based models for recurrent event data: Andersen and Gill (AG); Wei, Lin and Weissfeld (WLW); Prentice, Williams and Peterson, total time (PWP-CP) and gap time (PWP-GT); and Lee, Wei and Amato (LWA). Some authors have compared these models by observing di!erences that arise from "tting the models to real and simulated data. However, no attempt has been made to systematically identify the components of the models that are appropriate for recurrent event data. We propose a systematic way of characterizing such Cox-based models using four key components: risk intervals; baseline hazard; risk set, and correlation adjustment. From the de"nitions of risk interval and risk set there are conceptually seven such Cox-based models that are permissible, "ve of which are those previously identi"ed. The two new variant models are termed the &total time } restricted' (TT-R) and &gap time } unrestricted' (GT-UR) models. The aim of the paper is to determine which models are appropriate for recurrent event data using the key components. The models are "tted to simulated data sets and to a data set of childhood recurrent infectious diseases. The LWA model is not appropriate for recurrent event data because it allows a subject to be at risk several times for the same event. The WLW model overestimates treatment e!ect and is not recommended. We conclude that PWP-GT and TT-R are useful models for analysing recurrent event data, providing answers to slightly di!erent research questions. Further, applying a robust variance to any of these models does not adequately account for within-subject correlation.