Identifiability of Causal Effects for Binary Variables with Baseline Data Missing Due to Death | |
Yan, Wei ; Hu, Yaqin ; Geng, Zhi | |
2012 | |
关键词 | Causal inference Identifiability Missing covariate due to death Potential outcomes PRINCIPAL STRATIFICATION DESIGNS RANDOMIZED EXPERIMENTS INFERENCE MORTALITY OUTCOMES |
英文摘要 | We discuss identifiability and estimation of causal effects of a treatment in subgroups defined by a covariate that is sometimes missing due to death, which is different from a problem with outcomes censored by death. Frangakis et al. (2007, Biometrics 63, 641662) proposed an approach for estimating the causal effects under a strong monotonicity (SM) assumption. In this article, we focus on identifiability of the joint distribution of the covariate, treatment and potential outcomes, show sufficient conditions for identifiability, and relax the SM assumption to monotonicity (M) and no-interaction (NI) assumptions. We derive expectationmaximization algorithms for finding the maximum likelihood estimates of parameters of the joint distribution under different assumptions. Further we remove the M and NI assumptions, and prove that signs of the causal effects of a treatment in the subgroups are identifiable, which means that their bounds do not cover zero. We perform simulations and a sensitivity analysis to evaluate our approaches. Finally, we apply the approaches to the National Study on the Costs and Outcomes of Trauma Centers data, which are also analyzed by Frangakis et al. (2007) and Xie and Murphy (2007, Biometrics 63, 655658).; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000301924400014&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Biology; Mathematical & Computational Biology; Statistics & Probability; SCI(E); PubMed; 1; ARTICLE; 1; 121-128; 68 |
语种 | 英语 |
出处 | SCI ; PubMed |
出版者 | biometrics |
内容类型 | 其他 |
源URL | [http://hdl.handle.net/20.500.11897/157239] |
专题 | 数学科学学院 |
推荐引用方式 GB/T 7714 | Yan, Wei,Hu, Yaqin,Geng, Zhi. Identifiability of Causal Effects for Binary Variables with Baseline Data Missing Due to Death. 2012-01-01. |
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