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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 > > > > > > * > * 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/ * * 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/

**References**:**re: RE: st: propensity score analysis time-varying treatment***From:*"Ariel Linden, DrPH" <ariel.linden@gmail.com>

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