Causal inference in outcome-dependent two-phase sampling designs

• 成果类型: Article
• 署名作者: Wang, Weiwei; Scharfstein, Daniel; Tan, Zhiqiang; MacKenzie, Ellen J.
• 署名单位: Princeton University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Rutgers University System; Rutgers University New Brunswick
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2009.00712.x
• 发表日期: 2009
• 页码: 947-969
• 关键词: semiparametric regression-models; maximum-likelihood estimator; logistic-regression; statistics; validation

摘要

We consider estimation of the causal effect of a treatment on an outcome from observational data collected in two phases. In the first phase, a simple random sample of individuals is drawn from a population. On these individuals, information is obtained on treatment, outcome and a few low dimensional covariates. These individuals are then stratified according to these factors. In the second phase, a random subsample of individuals is drawn from each stratum, with known stratum-specific selection probabilities. On these individuals, a rich set of covariates is collected. In this setting, we introduce five estimators: simple inverse weighted; simple doubly robust; enriched inverse weighted; enriched doubly robust; locally efficient. We evaluate the finite sample performance of these estimators in a simulation study. We also use our methodology to estimate the causal effect of trauma care on in-hospital mortality by using data from the National Study of Cost and Outcomes of Trauma.