RE: st: propensity score analysis time-varying treatment

[CJ Wilson <cwil111111@hotmail.com>]

Hi Ariel, thank you very much for your response. I am basing the approach on the following references:
 
Leon AC, Demirtas H, Li C, Hedeker D. Two propensity score-based strategies for a three-decade observational study: investigating psychotropic medications and suicide risk. Stat Med. 012 Aug .. 

Also from the book chapter: Analysis of Observational Health Care Data Using SAS, "A Two-Stage Longitudinal Propensity Adjustment for Analysis of Observational Data" Andrew C. Leon, Donald Hedeker, Chunshan Li. 

----------------------------------------
> From: ariel.linden@gmail.com
> To: statalist@hsphsun..harvard.edu
> Subject: re: st: propensity score analysis time-varying treatment
> Date: Sun, 1 Nov 012 0::2::7 -500<
>
> 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 000;;1::50––0..
>
> Robins JM. Marginal structural models. In: 997 Proceedings of the Section
> on Bayesian Statistical Science. Alexandria, VA: American Statistical
> Association, 998::––0..
>
> Robins, J. M., and Hernan, M. A. 009.. Longitudinal Data Analysis, chap. 3::
> Estimation of the causal effects of time-varying exposures, 53--99.. New
> York: Chapman and Hall / CRC Press.
>
> Fewell Z, et al. Controlling for time-dependent confounding using marginal
> structural models. The Stata Journal (004)) ,, Number ,, pp. 02––20<
>
> 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 1 Number :: pp. 79--17<
>
> Ariel
>
>
> Date: Sat, 0 Nov 012 2::3::3 -800<
> From: CJ Wilson <cwil11111@@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:
> .. Estimate a random-intercept logistic regression model for the propensity
> of treatment using xtlogit
>
> .. Calculate the propensity score as follows:
> predict prop_score
> gen prop_score = exp(prop_score)/(++exp(prop_score))
> xtile ps_quintiles = prop_score,, nq())
> tabulate ps_quintiles, ge(q)
>
> .. Determine whether there is a treatment by propensity interaction
>
> Here are a couple areas where I'm struggling:
> .. 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?
> .. 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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