TY - JOUR UR - http://lib.ugent.be/catalog/pug01:1960901 ID - pug01:1960901 LA - eng TI - A simple unified approach for estimating natural direct and indirect effects PY - 2012 JO - (2012) AMERICAN JOURNAL OF EPIDEMIOLOGY SN - 0002-9262 PB - 2012 AU - Lange, Theis AU - Vansteelandt, Stijn AU - Bekaert, Maarten UGent 002004571987 AB - An important problem within both epidemiology and many social sciences is to break down the effect of a given treatment into different causal pathways and to quantify the importance of each pathway. Formal mediation analysis based on counterfactuals is a key tool when addressing this problem. During the last decade, the theoretical framework for mediation analysis has been greatly extended to enable the use of arbitrary statistical models for outcome and mediator. However, the researcher attempting to use these techniques in practice will often find implementation a daunting task, as it tends to require special statistical programming. In this paper, the authors introduce a simple procedure based on marginal structural models that directly parameterize the natural direct and indirect effects of interest. It tends to produce more parsimonious results than current techniques, greatly simplifies testing for the presence of a direct or an indirect effect, and has the advantage that it can be conducted in standard software. However, its simplicity comes at the price of relying on correct specification of models for the distribution of mediator (and exposure) and accepting some loss of precision compared with more complex methods. Web Appendixes 1 and 2, which are posted on the Journals Web site (http://aje.oupjournals.org/), contain implementation examples in SAS software (SAS Institute, Inc., Cary, North Carolina) and R language (R Foundation for Statistical Computing, Vienna, Austria). ER -Download RIS file
00000nam^a2200301^i^4500 | |||
001 | 1960901 | ||
005 | 20180813141239.0 | ||
008 | 111206s2012------------------------eng-- | ||
022 | a 0002-9262 | ||
024 | a 000306923800003 2 wos | ||
024 | a 1854/LU-1960901 2 handle | ||
024 | a 10.1093/aje/kwr525 2 doi | ||
040 | a UGent | ||
245 | a A simple unified approach for estimating natural direct and indirect effects | ||
260 | c 2012 | ||
520 | a An important problem within both epidemiology and many social sciences is to break down the effect of a given treatment into different causal pathways and to quantify the importance of each pathway. Formal mediation analysis based on counterfactuals is a key tool when addressing this problem. During the last decade, the theoretical framework for mediation analysis has been greatly extended to enable the use of arbitrary statistical models for outcome and mediator. However, the researcher attempting to use these techniques in practice will often find implementation a daunting task, as it tends to require special statistical programming. In this paper, the authors introduce a simple procedure based on marginal structural models that directly parameterize the natural direct and indirect effects of interest. It tends to produce more parsimonious results than current techniques, greatly simplifies testing for the presence of a direct or an indirect effect, and has the advantage that it can be conducted in standard software. However, its simplicity comes at the price of relying on correct specification of models for the distribution of mediator (and exposure) and accepting some loss of precision compared with more complex methods. Web Appendixes 1 and 2, which are posted on the Journals Web site (http://aje.oupjournals.org/), contain implementation examples in SAS software (SAS Institute, Inc., Cary, North Carolina) and R language (R Foundation for Statistical Computing, Vienna, Austria). | ||
598 | a A1 | ||
100 | a Lange, Theis | ||
700 | a Vansteelandt, Stijn u WE02 0 801001240647 | ||
700 | a Bekaert, Maarten u UGent 0 002004571987 0 802000246172 | ||
650 | a Mathematics and Statistics | ||
653 | a MARGINAL STRUCTURAL MODELS | ||
653 | a CAUSAL MEDIATION ANALYSIS | ||
653 | a mediation | ||
653 | a causal inference | ||
653 | a marginal structural models | ||
653 | a SENSITIVITY-ANALYSIS | ||
653 | a INFERENCE | ||
653 | a BIAS | ||
773 | t AMERICAN JOURNAL OF EPIDEMIOLOGY g Am. J. Epidemiol. 2012. 176 (3) p.190-195 q 176:3<190 | ||
856 | 3 Full Text u https://biblio.ugent.be/publication/1960901/file/6763080 z [ugent] y Lange_2012_AJE_176_3_190.pdf | ||
920 | a article | ||
Z30 | x WE 1 WE02 | ||
922 | a UGENT-WE |
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