Overview
Joseph G. Ibrahim is a biostatistician (University of North Carolina at Chapel Hill; formerly Harvard School of Public Health) known for Bayesian survival analysis, cure-rate models, power priors for historical data, missing-data methods, and Bayesian clinical-trial design.
Key Contributions / Features
- Promotion-time cure-rate model (Chen-Ibrahim-Sinha 1999): Bayesian surviving-fraction model with proportional hazards and valid improper-prior inference. Journal of the American Statistical Association 94(447): 909–919.
- Bayesian variable selection for logistic regression (Chen-Ibrahim-Yiannoutsos 1999): informative priors on the coefficients and the model space; posterior model probabilities from a single model's Gibbs output. Journal of the Royal Statistical Society, Series B 61(1): 223–242.
- Power prior (Ibrahim-Ryan-Chen 1998; Ibrahim-Chen 2000): informative priors built from historical data via a raised (discounted) likelihood.
- Bayesian Survival Analysis (Ibrahim-Chen-Sinha 2001) textbook.
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