33: Estimation of Quantile Treatment Effects in Historical Control Data Borrowing: A BNP approach

Music City Center 

Historical control data borrowing commonly focuses on estimation of the mean treatment effect. When the effect of covariates is of interest, the mean is computed conditional on covariates and called the conditional mean treatment effect. A mean treatment effect, however, may not adequately describe the impact of treatment when the distribution of the outcome is skewed or multimodal. This paper develops estimation of quantile treatment effects (QTEs), including conditional quantile treatment effects, in the context of historical control data borrowing. We use a Dirichlet process mixture model (DPMM) to estimate the density of the potential outcome given covariates, allowing for a flexible, data driven approach to capturing complex outcome distributions. The QTEs are estimated as the difference between the quantiles derived from the estimates of the treatment specific outcome distributions. Simulation studies demonstrate the performance of our method.

Bayesian non-parametric

Dirichlet process mixture models

data borrowing

quantile estimation

causal inference 

Biopharmaceutical Section