Causal Inference in Observational Studies with Outcome-Dependent Sampling
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Abstract:
In this paper, 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 are drawn from a population. On these individuals, information is obtained on treatment, outcome, and a few low-dimensional confounders. These individuals are then stratified according to these factors. In the second phase, a random sub-sample of individuals are drawn from each stratum, with known, stratum-specific selection probabilities. On these individuals, a rich set of confounding factors are collected. In this setting, we introduce four estimators: (1) simple inverse weighted, (2) locally efficient, (3) doubly robust and (4)enriched inverse weighted. 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 using data from the National Study of Cost and Outcomes of Trauma.
Subject Area:
Clinical Trials
Suggested Citation:
Weiwei Wang, Daniel Scharfstein, Zhiqiang Tan, and Ellen J. MacKenzie, "Causal Inference in Observational Studies with Outcome-Dependent Sampling" (June 2008). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 171.
http://www.bepress.com/jhubiostat/paper171