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RE: st: propensity score analysis time-varying treatment


From   CJ Wilson <cwil111111@hotmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: propensity score analysis time-varying treatment
Date   Thu, 15 Nov 2012 10:18:52 -0800

Hi Ariel, many thanks for your reply - it is very helpful. I really appreciate your time. If you don't mind, I just have one follow-up question - what does "propvar" represent in the ATE, ATT and the ATC formulas?
 
Thank you again!!!!
Carrie
 
> From: ariel.linden@gmail.com
> To: statalist@hsphsun2.harvard.edu
> Subject: re: RE: st: propensity score analysis time-varying treatment
> Date: Wed, 14 Nov 2012 11:49:50 -0500
> 
> Hi Carrie,
> 
> I just read through the Leon et al. article* (by the way, you gave an
> incomplete reference. See below for the entire reference). The method is not
> really longitudinal per-se (in the classical sense), but comparing
> non-treated "periods" to treated "periods". I don't find this approach to be
> as intuitive as the weighting approaches I gave you references for below. At
> the very least, I think you should read through the following article:
> 
> Hernán MA, Brumback B, Robins JM. Estimating the causal effect of zidovudine
> on CD4 count with a marginal structural model for repeated measures. Stats
> in Med 2002;21:1689-1709.
> 
> All that said, Leon et al., describe how the propensity score was estimated
> in section 2.2.1.
> 
> "Because subjects could have multiple exposure intervals, in which during
> each interval they either received treatment or did not,
> a longitudinal implementation of the propensity score that has been shown to
> reduce selection bias [15] was applied here:
> 
> [equation]
> 
> Propensity scores were calculated using parameter estimates from
> mixed-effects logistic regression analyses, which included a random subject
> effect to account for the multiple observations within each subject."
> 
> Next, they created quintiles of the propensity score and then tabulated the
> number of periods exposed and unexposed for each quintile (table 1). Table 2
> then shows the outcomes for each of these exposed vs unexposed categories.
> 
> In regards to your initial questions:
> 
> First, -predict- already gives you the predicted value (probability of
> treatment) you need for analysis. There is no need to run -generate-.
> 
> Next, while you could run standardized differences here (and for that I
> would suggest a user written program -pbalchk- [findit pbalchk], I am not
> convinced of its utility here. After all, these are the same individuals in
> both groups, so what would you expect to see different?
> 
> As for you last question about average treatment effects for the untreated
> (or controls), again it points to a weighted model as an alternative to the
> approach in the Leon et al. paper. Using weighting, you could estimate the
> ATE (also known as IPTW), the ATT or the ATC with the following respective
> code:
> 
> gen iptw = cond(treatvar, 1/propvar, 1/(1-propvar))
> gen att = cond(treatvar, 1, propvar/(1- propvar))
> gen atc = cond(treatvar, (1-propvar)/propvar, 1)
> 
> 
> I hope this helps
> 
> Ariel
> 
> * 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. Statist. Med. 2012, 31 3255–3260
> 
> 
> 
> Date: Tue, 13 Nov 2012 19:31:32 -0800
> From: CJ Wilson <cwil111111@hotmail.com>
> Subject: RE: st: propensity score analysis time-varying treatment
> 
> 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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