In a regression context, consider the difference in expected outcome associated with a particular difference in one of the input variables. If the true regression relationship involves interactions, then this "predictive comparison" can depend on the values of the other input variables....

Case-control data with imprecise exposure measurements can be analyzed via Bayesian fitting of a retrospective discriminant analysis model. The parameters of interest are the regression coefficients in the prospective log-odds ratio for disease. Under a standard noninformative prior, the...

Realistic statistical modelling of observational data often suggests a statistical model which is not fully identified, owing to potential biases that are not under the control of study investigators. Bayesian inference can be implemented with such a model, ideally with the most precise prior...

Marginal structural models (MSM) can be used to estimate the effect of a time dependent exposure in presence of time dependent confounding. Previously Fewell et al. (2004) described how to estimate this model in Stata based on a weighted pooled logistic model approximation. However, based on the...

The bivariate normal density with unit variance and correlation ρ is well known. We show that by integrating out ρ, the result is a function of the maximum norm. The Bayesian interpretation of this result is that if we put a uniform prior over ρ, then the marginal bivariate density depends...

A commonly used method for evaluating a hospital’s performance on an outcome is to compare the hospital’s observed outcome rate to the hospital’s expected outcome rate given its patient (case) mix and service. The process of calculating the hospital’s expected outcome rate given its...

Background:It has become common practice to analyze randomized experiments using linear regression with covariates. Improved precision of treatment effect estimates is the usual motivation. In a series of important articles, David Freedman showed that this approach can be badly flawed. Recent...