Data-Adaptive Selection of the Propensity Score Truncation Level for Inverse-Probability –Weighted and Targeted Maximum Likelihood Estimators of Marginal Point Treatment Effects

We present a simple truncation strategy based on the sample size,n, that sets the upper bound on IP weights at $\sqrt{\textit{n}}$ ln  n/5. For TMLE, the lower bound on the PS should be set to 5/($\sqrt{\textit{n}}$ ln  n/5). Our strategy was designed to optimize the mean squared error of the parameter estimate. It naturally extends to data structures with missing outcomes. Simulation studies and a data analysis demonstrate our strategy ’s ability to minimize both bias and mean squared error in comparison with other common strategies, including the popular but flawed quantile-based heuristic.

Source: American Journal of Epidemiology - May 4, 2022 Category: Epidemiology Source Type: research