A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.






Statistics in Infectious Disease Research


Guest lecture on some of the ways that statistics is used in infectious disease research in the Computational Biology course at Roanoke Valley Governor’s School.

Model-Agnostic Variable Importance and Selection


I discuss a framework for inference on general model-agnostic variable importance measures, and how this framework can be used to perform variable selection. I also briefly discuss several directions of current work, including longitudinal variable importance; a measure of how important variables are for tailoring treatment; and fairness-aware variable importance.

Inference for Model-Agnostic Variable Importance


In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response – in other words, to gauge the variable importance of features. In this talk, I will discuss a model-agnostic notion of variable importance and general conditions under which valid inference on the true importance can be obtained, even when machine learning-based techniques are used as part of estimation. We define variable importance as a population-level contrast between the oracle predictiveness of all available features versus all features except those under consideration. I provide several examples of predictiveness measures, including for right-censored outcomes, and illustrate the use of the proposed methods with data from a study of an antibody against HIV-1 infection.


Design considerations for subgroup analyses in cluster-randomized trials based on aggregated individual-level predictors


In research assessing the effect of an intervention or exposure, a key secondary objective often involves assessing differential effects of this intervention or exposure in subgroups of interest; this is often referred to as assessing effect modification or heterogeneity of treatment effects (HTE). Observed HTE can have important implications for policy, including intervention strategies (e.g., will some patients benefit more from intervention than others?) and prioritizing resources (e.g., to reduce observed health disparities). Analysis of HTE is well understood in studies where the independent unit is an individual. In contrast, in studies where the independent unit is a cluster (e.g., a hospital or school) and a cluster-level outcome is used in the analysis, it is less well understood how to proceed if the HTE analysis of interest involves an individual-level characteristic (e.g., self-reported race) that must be aggregated at the cluster level. Through simulations, we show that only individual-level models have power to detect HTE by individual-level variables; if outcomes must be defined at the cluster level, then there is often low power to detect HTE by the corresponding aggregated variables. We illustrate the challenges inherent to this type of analysis in a study assessing the effect of an intervention on increasing COVID-19 booster vaccination rates at long-term care centers.