Chronic medical conditions are often manifested by the incidence of recurrent adverse clinical events. In clinical trials designed to investigate therapeutic interventions for such conditions it is natural to make treatment comparisons on the basis of event occurrence. However, when there is a more
rhDNase as an example of recurrent event analysis
β Scribed by Terry M. Therneau; Scott A. Hamilton
- Publisher
- John Wiley and Sons
- Year
- 1997
- Tongue
- English
- Weight
- 172 KB
- Volume
- 16
- Category
- Article
- ISSN
- 0277-6715
No coin nor oath required. For personal study only.
β¦ Synopsis
We consider counting process methods for analysing time-to-event data with multiple or recurrent outcomes, using the models developed by Anderson and Gill, Wei, Lin and Weissfeld and Prentice, Williams and Peterson. We compare the methods, and show how to implement them using popular statistical software programs. By analysing three data sets, we illustrate the strengths and pitfalls of each method. The first example is simulated and involves the effect of a hidden covariate. The second is based on a trial of gamma interferon, and behaves remarkably like the first. The third and most interesting example involves both multiple events and discontinuous intervals at risk, and the three approaches give dissimilar answers. We recommend the AG and marginal models for the analysis of this type of data.
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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
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