It is becoming increasingly clear that producing causal estimates from studies with acceptable internal validity is not sufficient to guide interventions and policy analysis for population health. External validity is critical for applying internally valid results from a study population to a target population that may or may not have given rise to the study population. Novel developments in causal inference allow us to give the sufficient and necessary conditions for generalizability and transportability. This workshop will provide accessible theoretical and practical introduction to the concepts of internal and external validity and show to generalize or transport internally valid external estimates from study populations to source or target populations. The concept of data fusion will be introduced to workshop participants for the purposes of generalizing or transporting data and effect estimates across populations and settings. The workshop will use structural and graphical language to make it accessible to epidemiologists interested in causal inference for informing interventions and policy. It will show how g-methods, particularly g-computation and inverse-probability-weighting and inverse-odds-weighting with(out) augmentation, can be used to generalize or transport effect estimates. Ample applications using empirical datasets and software codes will be provided in SAS, Stata and R.
Add to Calendar