TY - JOUR
AU1 - Cheng, Jing
AU2 - Small, Dylan S.
AU3 - Tan, Zhiqiang
AU4 - Ten Have, Thomas R.
AB - Causal approaches based on the potential outcome framework provide a useful tool for addressing noncompliance problems in randomized trials. We propose a new estimator of causal treatment effects in randomized clinical trials with noncompliance. We use the empirical likelihood approach to construct a profile random sieve likelihood and take into account the mixture structure in outcome distributions, so that our estimator is robust to parametric distribution assumptions and provides substantial finite-sample efficiency gains over the standard instrumental variable estimator. Our estimator is asymptotically equivalent to the standard instrumental variable estimator, and it can be applied to outcome variables with a continuous, ordinal or binary scale. We apply our method to data from a randomized trial of an intervention to improve the treatment of depression among depressed elderly patients in primary care practices.
TI - Efficient nonparametric estimation of causal effects in randomized trials with noncompliance
JF - Biometrika
DO - 10.1093/biomet/asn056
DA - 2009-03-21
UR - https://www.deepdyve.com/lp/oxford-university-press/efficient-nonparametric-estimation-of-causal-effects-in-randomized-8yVyYCudCi
SP - 19
EP - 36
VL - 96
IS - 1
DP - DeepDyve
ER -