In trials with failure-time outcomes, statistical information is determined by accumulated events. Interim and final analyses are performed after a prespecified number of events have been observed. It is of interest to predict when a prespecified number of events will be observed based on accumulating data. We propose a fully Bayesian nonparametric approach in modeling the survival probabilities. We compare the accuracy and precision of this approach to proposed parametric and semi-parametric methods. In summary, the proposed method offers greater flexibility and based on our studies has the ability to match or outperform existing methods.
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| http://dx.doi.org/10.1080/10543406.2014.920853 | DOI Listing |
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