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Often in public health, we are interested in the treatment effect of anintervention on a population that is systemically different from theexperimental population the intervention was originally evaluated in. Whentreatment effect heterogeneity is present in a randomized controlled trial,generalizing the treatment effect from this experimental population to a targetpopulation of interest is a complex problem; it requires the characterizationof both the treatment effect heterogeneity and the baseline covariate mismatchbetween the two populations. Despite the importance of this problem, theliterature for variable selection in this context is limited. In this paper, wepresent a Group LASSO-based approach to variable selection in the context oftreatment effect generalization, with an application to generalize thetreatment effect of very low nicotine content cigarettes to the overall U.S.smoking population.

Group LASSO Variable Selection Method for Treatment Effect Generalization