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We use an Expectation\u2013Maximization (EM) algorithm where the multiplicative event probability is viewed as the joint probability for a collection of latent binary outcomes. This gives a simple iterative scheme that provides stable convergence to the maximum likelihood estimate. In addition to reliability, the method offers some flexible generalizations, including models with unspecified isotonic regression functions. We examine the method's performance using simulations and data analyses of the age-specific RR of mortality following heart attack. These analyses demonstrate the potential for numerical instability in RR regression and show how this can be overcome using the proposed approach. Source code to implement the method in R is provided as supplementary material available at Biostatistics online.<\/jats:p>","DOI":"10.1093\/biostatistics\/kxr030","type":"journal-article","created":{"date-parts":[[2011,9,14]],"date-time":"2011-09-14T03:22:01Z","timestamp":1315970521000},"page":"179-192","source":"Crossref","is-referenced-by-count":72,"title":["Relative risk regression: reliable and flexible methods for log-binomial models"],"prefix":"10.1093","volume":"13","author":[{"given":"Ian C.","family":"Marschner","sequence":"first","affiliation":[{"name":"Department of Statistics, Macquarie University, NSW 2109, Australia and National Health and Medical Research Council Clinical Trials Centre, University of Sydney, NSW 2006, Australia , ian.marschner@mq.edu.au"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandra C.","family":"Gillett","sequence":"additional","affiliation":[{"name":"Department of Statistics, Macquarie University, NSW 2109, 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