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From |
"Ariel Linden, DrPH" <ariel.linden@gmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
re: st: Mediation for a left-censored dependent variable and ordinal mediator variable |

Date |
Mon, 13 Feb 2012 11:27:20 -0500 |

Hi Vijay, I believe you are using the wrong modeling approach here. You need a "specialized" program to correctly model an ordinal mediator and censored outcome. Using -sem- or any other regression approach that assumes linearity in these two models will likely lead to misspecification and thus erroneous results. You can consider using a user written program -khb- (findit khb) than can handle ordinal mediators, but does not appear to handle censored outcomes at this time. Similarly, you could consider using the R version of -medeff- (findit medeff), that can also handle a large array of mediator and outcome data types (the Stata version is limited to regress, logit and probit). I am not sure if this program can handle censored cases. Perhaps another approach, albeit a manual process at this point, would be to use a potential outcomes approach via stratification, weighting, or principal stratification (see references below). I am particularly enamored with Hong's stratification approach, and I think it would fit your needs the best. However, it (like the rest of the approaches referenced below) is somewhat labor intensive.... I hope this helps Ariel Hong, G. (2010). Ratio of mediator probability weighting for estimating natural direct and indirect effects. 2010 Proceedings of the American Statistical Association, Biometrics Section [pp.2401-2415], Alexandria, VA: American Statistical Association. Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15(4), 309-334. Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science, 25(1), 51-71. Pearl, J. (2010). The mediation formula: A guide to the assessment of causal pathways in non-linear models. Los Angeles, CA: University of California, Los Angeles. Technical report R-363, July 2010. Peterson, M. L., Sinisi, S. E., & van der Laan, M. J. (2006). Estimation of direct causal effects. Epidemiology, 17(3), 276-284. VanderWeele, T.J. (2009). Marginal structural models for the estimation of direct and indirect effects. Epidemiology, 20, 18-26. Jo, B. (2008). Causal inference in randomized experimentswith mediational processes. Psychological Methods, 13, 314-336. Jo, B., Stuart, E. A., MacKinnon, D. P., & Vinokur, A. D. (2011). The use of propensity scores in mediation analysis. Multivariate Behavioral Research, 46, 425-452. Date: Sun, 12 Feb 2012 23:00:57 -0500 From: Vijay Sampath <vijsampath@hotmail.com> Subject: st: Mediation for a left-censored dependent variable and ordinal mediator variable I am trying to run a mediation analysis in Stata 12 using the "sem" command. The dependent variable is a continuous variable which is left-censored. The mediator is a ordinal variable with 5 levels and the independent variable is a log-converted continuous variable. I am getting significant results when I run the regressions separately: (a) the tobit regressions on the dependent variable, and (b) ordered logit regressions with the mediator as the dependent variable. I would like any thoughts as to how to combine them. I did a Google search, which pointed me to the MPlus command. Thanks, VJ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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