title: Nonparametric Prediction of Event Times for Analysis of Failure-Time Data.
abstract: 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.
authors.affiliation: a Department of Biostatistics , Boston University , Roxbury , Massachusetts , USA.
authors.forename: Stephanie
authors.initials: S
authors.lastname: Lustgarten
authors.forename: Gheorghe
authors.initials: G
authors.lastname: Doros
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D005544:Forecasting; D006801:Humans; D018709:Statistics, Nonparametric; D013997:Time Factors; D017211:Treatment Failure
keywords: Bayesian; Censoring; Nonparametric; Survival