Estimating the principal stratum direct effect when the total effects are consistent between two standard populations

被引:2
作者
Chiba, Yasutaka [1 ]
机构
[1] Kinki Univ, Sch Med, Dept Environm Med & Behav Sci, Osaka 5898511, Japan
关键词
Causal inference; Intention-to-treat; Potential outcome; Randomized trial; INTERMEDIATE VARIABLES; CAUSAL; INFERENCE; STRATIFICATION; OUTCOMES;
D O I
10.1016/j.spl.2010.02.008
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Adjusting for an intermediate variable is a common analytic strategy for estimating a direct effect. Even if the total effect is unconfounded, the direct effect is not identified when unmeasured variables affect the intermediate and outcome variables. This paper focuses on the application of the principal stratification approach for estimating the direct effect of a randomized treatment. The approach is used to evaluate the direct effect of treatment as the difference between the expectations of potential outcomes within latent subgroups of subjects for which the intermediate variable would be constant, regardless of the randomized treatment assignment. To derive an estimator of the direct effect in cases in which the treatment and intermediate variables are dichotomous, we assume that the total effects are consistent between two standard populations. This assumption implies that the total effects are equal between two subpopulations with the same treatment assignment and a different intermediate behavior, or the total effects are equal between two subpopulations with a different treatment assignment and the same intermediate behavior. We show that the direct effect corresponds to the standard intention-to-treat effect under this assumption. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:958 / 961
页数:4
相关论文
共 13 条
[1]  
Angrist JD, 1996, J AM STAT ASSOC, V91, P444, DOI 10.2307/2291629
[2]   Bounds on direct effects in the presence of confounded intermediate variables [J].
Cai, Zhihong ;
Kuroki, Manabu ;
Pearl, Judea ;
Tian, Jin .
BIOMETRICS, 2008, 64 (03) :695-701
[3]   Principal stratification in causal inference [J].
Frangakis, CE ;
Rubin, DB .
BIOMETRICS, 2002, 58 (01) :21-29
[4]   Mediation analysis with principal stratification [J].
Gallop, Robert ;
Small, Dylan S. ;
Lin, Julia Y. ;
Elliott, Michael R. ;
Joffe, Marshall ;
Ten Have, Thomas R. .
STATISTICS IN MEDICINE, 2009, 28 (07) :1108-1130
[5]   Causal vaccine effects on binary postinfection outcomes [J].
Hudgens, MG ;
Halloran, ME .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2006, 101 (473) :51-64
[6]   Improved estimation of controlled direct effects in the presence of unmeasured confounding of intermediate variables [J].
Kaufman, S ;
Kaufman, JS ;
MacLehose, RF ;
Greenland, S ;
Poole, C .
STATISTICS IN MEDICINE, 2005, 24 (11) :1683-1702
[7]  
Pearl J., 2009, MODELS REASONING INF
[8]   Estimation of direct causal effects [J].
Petersen, ML ;
Sinisi, SE ;
van der Laan, MJ .
EPIDEMIOLOGY, 2006, 17 (03) :276-284
[9]   IDENTIFIABILITY AND EXCHANGEABILITY FOR DIRECT AND INDIRECT EFFECTS [J].
ROBINS, JM ;
GREENLAND, S .
EPIDEMIOLOGY, 1992, 3 (02) :143-155