QQ客服 官方微博 反馈留言

	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.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。