Biostatistics Weekly Seminar

It is surprisingly challenging to fit mixed models to data from complex samples, because the log likelihood is not just a sum over observations, and because sampling interacts confusingly with the bias: variance tradeoffs of BLUPs. Simply modeling the design variables is possible, but requires that the design variables are available (often untrue in public-use data) and that they are legitimate predictor variables (eg, not in the causal pathway). The design-based approach in use at the moment requires that the sampling units in the design are the same as the clusters in the model, which is a strong restriction. I will describe an approach that allows arbitrary models and designs, and an R implementation that currently allows multilevel linear models and multistage samples. The approach is based on composite likelihood, generalizing an idea by JNK Rao and co-workers. Our motivating example, family genetic models in the Hispanic Community Health Survey, is still not accessible but is within sight.

MRBIII, Room 1220
05 February 2020
1:30pm