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re: st: propensity score analysis time-varying treatment


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 



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