Abstract
There has been a renewed interest in identifying heterogenous treatment effects (HTEs) to guide personalized medicine. The objective was to illustrate the use of a step-by-step transparent parametric data-adaptive approach (the generalized HTE approach) based on the G-computation algorithm to detect heterogenous subgroups and estimate meaningful conditional average treatment effects (CATE). The following seven steps implement the generalized HTE approach: Step 1: Select variables that satisfy the backdoor criterion and potential effect modifiers; Step 2: Specify a flexible saturated model including potential confounders and effect modifiers; Step 3: Apply a selection method to reduce overfitting; Step 4: Predict potential outcomes under treatment and no treatment; Step 5: Contrast the potential outcomes for each individual; Step 6: Fit cluster modeling to identify potential effect modifiers; Step 7: Estimate subgroup CATEs. We illustrated the use of this approach using simulated and real data. Our generalized HTE approach successfully identified HTEs and subgroups defined by all effect modifiers using simulated and real data. Our study illustrates that it is feasible to use a step-by-step parametric and transparent data-adaptive approach to detect effect modifiers and identify meaningful HTEs in an observational setting. This approach should be more appealing to epidemiologists interested in explanation.