Quantitative exposures (e.g. smoking, alcohol consumption) in predicting binary health outcomes (e.g. mortality, incidence of a disease) are frequently categorized and modeled with indicator variables. Results are expressed as relative risks for the levels of exposure using one category as referent. Dose-response meta-analysis is an increasing popular statistical technique that aims to estimate and characterize an overall functional relation from such aggregated data. A common approach is to contrast the outcome risk in the highest exposure category relative to the lowest. A dose-response approach is more robust since it takes into account the quantitative values associated with the exposure categories. It provides a detailed description of how the risk varies throughout the observed range of exposure. Additionally, since all the exposure categories contribute to determine the overall relation, estimation is more efficient. Our aim is to give a short introduction to the methodological framework (structure of aggregated data, covariance of correlated outcomes, estimation and pooling of individual curves). We describe how to test hypothesis and how to quantify statistical heterogeneity. Alternative tools to flexibly model the quantitative exposure will be presented (splines and polynomials). We will illustrate modelling techniques and presentation of (graphical and tabular) results using the dosresmeta R package.
I like teaching nonparametric multivariate analyses to biologists.
Reproducible research is really important to me and I make all my work available online, mostly as Rmd files. I still like to code, use Github and shiny as well as Bioconductor. I am trying to finish a book for biologists... Read More →
