A Bayesian approach to a logistic regression model with incomplete information
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Updated: Jun 30, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Taeryon Choi1, Mark J Schervish, Ketra A Schmitt
1Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, Maryland 21250, USA. tchoi@math.umbc.edu
Abstract:
We consider a set of independent Bernoulli trials with possibly different success probabilities that depend on covariate values. However, the available data consist only of aggregate numbers of successes among subsets of the trials along with all of the covariate values. We still wish to estimate the parameters of a modeled relationship between the covariates and the success probabilities, e.g., a logistic regression model. In this article, estimation of the parameters is made from a Bayesian perspective by using a Markov chain Monte Carlo algorithm based only on the available data. The proposed methodology is applied to both simulation studies and real data from a dose-response study of a toxic chemical, perchlorate.
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