Relative risk regression: reliable and flexible methods for log-binomial models

On the Estimation of Relative Risks via Log Binomial Regression

Given the well known convergence difficulties in fitting log binomial regression with standard GLM software, we implement a direct solution via constrained optimization which avoids the circumventions found in the literature. The use of a log binomial model is motivated by our interest in directly estimating relative risks adjusted for confounders. A Bayesian log binomial regression model is al...

Negative Binomial Regression Models and Estimation Methods

The Poisson-Gamma model has properties that are very similar to the Poisson model discussed in Appendix C, in which the dependent variable i y is modeled as a Poisson variable with a mean i  where the model error is assumed to follow a Gamma distribution. As it names implies, the Poisson-Gamma is a mixture of two distributions and was first derived by Greenwood and Yule (1920). This mixture di...

Flexible Regression Models and Relative Forecast Performance ∗

Flexible regression models for rate differences, risk differences and relative risks.

Generalized additive models (GAMs) based on the binomial and Poisson distributions can be used to provide flexible semi-parametric modelling of binary and count outcomes. When used with the canonical link function, these GAMs provide semi-parametrically adjusted odds ratios and rate ratios. For adjustment of other effect measures, including rate differences, risk differences and relative risks,...

Estimation of Count Data using Bivariate Negative Binomial Regression Models

Abstract Negative binomial regression model (NBR) is a popular approach for modeling overdispersed count data with covariates. Several parameterizations have been performed for NBR, and the two well-known models, negative binomial-1 regression model (NBR-1) and negative binomial-2 regression model (NBR-2), have been applied. Another parameterization of NBR is negative binomial-P regression mode...