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

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

Subject |
re: st: propensity score analysis time-varying treatment |

Date |
Sun, 11 Nov 2012 10:02:47 -0500 |

Carrie, I am not sure why you have decided to use stratification when your data is longitudinal and treatment is time-varying? What would your rationale be for that? A more sensible approach would be to use a marginal structural model or g-computation. I suggest you read the references below. Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiol 2000;11:550?60. Robins JM. Marginal structural models. In: 1997 Proceedings of the Section on Bayesian Statistical Science. Alexandria, VA: American Statistical Association, 1998:1?10. Robins, J. M., and Hernan, M. A. 2009. Longitudinal Data Analysis, chap. 23: Estimation of the causal effects of time-varying exposures, 553-599. New York: Chapman and Hall / CRC Press. Fewell Z, et al. Controlling for time-dependent confounding using marginal structural models. The Stata Journal (2004) 4, Number 4, pp. 402?420 Daniel RM, et al. gformula: Estimating causal effects in the presence of time-varying confounding or mediation using the g-computation formula. The Stata Journal Volume 11 Number 4: pp. 479-517 Ariel Date: Sat, 10 Nov 2012 12:23:03 -0800 From: CJ Wilson <cwil111111@hotmail.com> Subject: st: propensity score analysis time-varying treatment Hi all, I posted the below message awhile ago but didn't get any responses. Any help at all would be much appreciated. thanks! Dear Statalist, I?m trying to determine the correct Stata code to conduct a propensity score stratification analysis in a longitudinal dataset. My variable for the treatment is time-varying, so some patients receive treatment at certain time points but not others. I haven?t been successful at finding any examples of Stata code online that are for longitudinal propensity scores. Here is my proposed approach: 1. Estimate a random-intercept logistic regression model for the propensity of treatment using xtlogit 2. Calculate the propensity score as follows: predict prop_score gen prop_score2 = exp(prop_score)/(1+exp(prop_score)) xtile ps_quintiles = prop_score2, nq(5) tabulate ps_quintiles, ge(q) 3. Determine whether there is a treatment by propensity interaction Here are a couple areas where I'm struggling: 4. I would like to determine the standardized difference in means to assess whether balance is achieved. Does anyone have any sample Stata code relating to this? 5. Does anyone have sample code to determine the average treatment effect among the untreated? Any advice would be much appreciated. Thank you!! Carrie * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: propensity score analysis time-varying treatment***From:*CJ Wilson <cwil111111@hotmail.com>

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