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.
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。
相似文献
1
Estimation and inference for the causal effect of receiving treatment on a multinomial outcome: an alternative approach.
Biometrics. 2011 Mar;67(1):319-23; discussion 323-5. doi: 10.1111/j.1541-0420.2010.01451_1.x.
2
Estimation and inference for the causal effect of receiving treatment on a multinomial outcome.
Biometrics. 2009 Mar;65(1):96-103. doi: 10.1111/j.1541-0420.2008.01020.x. Epub 2008 Mar 29.
3
Likelihood-based inference for bounds of causal parameters.
Stat Med. 2018 Dec 30;37(30):4695-4706. doi: 10.1002/sim.7949. Epub 2018 Aug 28.
4
Causal proportional hazards models and time-constant exposure in randomized clinical trials.
Lifetime Data Anal. 2005 Dec;11(4):435-49. doi: 10.1007/s10985-005-5233-z.
5
Does Cox analysis of a randomized survival study yield a causal treatment effect?
Lifetime Data Anal. 2015 Oct;21(4):579-93. doi: 10.1007/s10985-015-9335-y. Epub 2015 Jun 24.
6
Identifiability and estimation of causal effects in randomized trials with noncompliance and completely nonignorable missing data.
Biometrics. 2009 Sep;65(3):675-82. doi: 10.1111/j.1541-0420.2008.01120.x. Epub 2008 Aug 28.
7
Estimating the causal effect of randomization versus treatment preference in a doubly randomized preference trial.
Psychol Methods. 2012 Jun;17(2):244-54. doi: 10.1037/a0028031. Epub 2012 May 7.
8
Joint modeling compliance and outcome for causal analysis in longitudinal studies.
Stat Med. 2014 Sep 10;33(20):3453-65. doi: 10.1002/sim.5811. Epub 2013 Apr 9.
9
Causal models for randomized trials with two active treatments and continuous compliance.
Stat Med. 2011 Aug 30;30(19):2349-62. doi: 10.1002/sim.4296. Epub 2011 Jul 12.
10
Incorporating prognostic factors into causal estimators: a comparison of methods for randomised controlled trials with a time-to-event outcome.
Stat Med. 2012 Nov 20;31(26):3073-88. doi: 10.1002/sim.5411. Epub 2012 Jun 19.
引用本文的文献
1
A Bayesian Hierarchical CACE Model Accounting for Incomplete Noncompliance With Application to a Meta-analysis of Epidural Analgesia on Cesarean Section.
J Am Stat Assoc. 2021;116(536):1700-1712. doi: 10.1080/01621459.2021.1900859. Epub 2021 Apr 27.
2
Estimating the Complier Average Causal Effect in a Meta-Analysis of Randomized Clinical Trials With Binary Outcomes Accounting for Noncompliance: A Generalized Linear Latent and Mixed Model Approach.
Am J Epidemiol. 2022 Jan 1;191(1):220-229. doi: 10.1093/aje/kwab238.
3
A Bayesian hierarchical model estimating CACE in meta-analysis of randomized clinical trials with noncompliance.
Biometrics. 2019 Sep;75(3):978-987. doi: 10.1111/biom.13028. Epub 2019 Apr 4.
4
Latent class instrumental variables: a clinical and biostatistical perspective.
Stat Med. 2016 Jan 15;35(1):147-60. doi: 10.1002/sim.6612. Epub 2015 Aug 4.
5
Semiparametric transformation models for causal inference in time to event studies with all-or-nothing compliance.
J R Stat Soc Series B Stat Methodol. 2015 Mar 1;77(2):397-415. doi: 10.1111/rssb.12072.
6
A comparison of three methods of Mendelian randomization when the genetic instrument, the risk factor and the outcome are all binary.
PLoS One. 2012;7(5):e35951. doi: 10.1371/journal.pone.0035951. Epub 2012 May 9.
7
Predicting treatment effect from surrogate endpoints and historical trials: an extrapolation involving probabilities of a binary outcome or survival to a specific time.
Biometrics. 2012 Mar;68(1):248-57. doi: 10.1111/j.1541-0420.2011.01646.x. Epub 2011 Aug 13.
8
Clarifying the role of principal stratification in the paired availability design.
Int J Biostat. 2011;7(1):25. doi: 10.2202/1557-4679.1338. Epub 2011 May 20.
本文引用的文献
1
Reproducible research and Biostatistics.
Biostatistics. 2009 Jul;10(3):405-8. doi: 10.1093/biostatistics/kxp014.
2
Estimation and inference for the causal effect of receiving treatment on a multinomial outcome.
Biometrics. 2009 Mar;65(1):96-103. doi: 10.1111/j.1541-0420.2008.01020.x. Epub 2008 Mar 29.
3
Simple maximum likelihood estimates of efficacy in randomized trials and before-and-after studies, with implications for meta-analysis.
Stat Methods Med Res. 2005 Aug;14(4):349-67. doi: 10.1191/0962280205sm404oa.
4
Principal stratification in causal inference.
Biometrics. 2002 Mar;58(1):21-9. doi: 10.1111/j.0006-341x.2002.00021.x.
5
The paired availability design: a proposal for evaluating epidural analgesia during labor.
Stat Med. 1994 Nov 15;13(21):2269-78. doi: 10.1002/sim.4780132108.
6
On estimating efficacy from clinical trials.
Stat Med. 1991 Jan;10(1):45-52. doi: 10.1002/sim.4780100110.
7
The case-control design and the assessment of the efficacy of cancer screening.
J Clin Epidemiol. 1991;44(11):1215-21. doi: 10.1016/0895-4356(91)90154-2.
Zotero 插件