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接受治疗对多项结果的因果效应的估计与推断：一种替代方法。

Estimation and inference for the causal effect of receiving treatment on a multinomial outcome: an alternative approach.

作者信息

Baker Stuart G

机构信息

Biometry Research Group, Division of Cancer Prevention, National Cancer Institute, EPN 3131, 6130 Executive Blvd MSC 7354, Bethesda, Maryland 20892-7354, USA.

出版信息

Biometrics. 2011 Mar;67(1):319-23; discussion 323-5. doi: 10.1111/j.1541-0420.2010.01451_1.x.

DOI:10.1111/j.1541-0420.2010.01451_1.x
PMID:20560933
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3030650/
Abstract

Recently, Cheng (2009, Biometrics 65, 96-103) proposed a model for the causal effect of receiving treatment when there is all-or-none compliance in one randomization group, with maximum likelihood estimation based on convex programming. We discuss an alternative approach that involves a model for all-or-none compliance in two randomization groups and estimation via a perfect fit or an expectation-maximization algorithm for count data. We believe this approach is easier to implement, which would facilitate the reproduction of calculations.

摘要

最近，程（2009年，《生物统计学》65卷，96 - 103页）提出了一个模型，用于在一个随机分组中存在全有或全无依从性时接受治疗的因果效应，该模型基于凸规划进行最大似然估计。我们讨论了另一种方法，该方法涉及两个随机分组中的全有或全无依从性模型，并通过计数数据的完全拟合或期望最大化算法进行估计。我们认为这种方法更易于实施，这将有助于计算结果的重现。