Incremental intervention effects in studies with dropout and many timepoints

作     者：Kim, Kwangho Kennedy, Edward H. Naimi, Ashley I. 

作者机构：Department of Statistics & Data Science Machine Learning Department Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Department of Statistics & Data Science Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Department of Epidemiology Rollins School of Public Health Emory University AtlantaGA United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2019年

核心收录：

摘      要：Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by dropout and positivity violations. We tackle these problems by generalizing effects of recent incremental interventions (which shift propensity scores rather than set treatment values deterministically) to accommodate multiple outcomes and subject dropout. We give an identifying expression for incremental intervention effects when dropout is conditionally ignorable (without requiring treatment positivity), and derive the nonparametric efficiency bound for estimating such effects. Then we present efficient nonparametric estimators, showing that they converge at fast parametric rates and yield uniform inferential guarantees, even when nuisance functions are estimated flexibly at slower rates. We also study the variance ratio of incremental intervention effects relative to more conventional deterministic effects in a novel infinite time horizon setting, where the number of timepoints can grow with sample size, and show that incremental intervention effects yield near-exponential gains in statistical precision in this setup. Finally we conclude with simulations and apply our methods in a study of the effect of low-dose aspirin on pregnancy *** Codes 62G05 Copyright © 2019, The Authors. All rights reserved.