similar to: propensity score adjustment using R

Dear All, My question is more a statistical question than a R question. The reason I am posting here is that there are lots of excellent statistician on this list, who can always give me good advices. Per my understanding, the purpose of propensity score is to reduce the bias while estimating the treatment effect and its implementation is a 2-stage model. 1) First of all, if we assume that T =

Hi all, i am wondering if there?s any other method to adjust for selection related bias of estimates except propensity scoring and heckit / mills ratio approach? i also read documentation of Match and twang package so far, so i don?t speak of any ATE / ATT related methods, respectively any methods that match or stratify... Is there something else ? thx in advance

Dear R-list, i have 6 different sets of samples. Each sample has about 5000 observations, with each observation comprised of 150 baseline covariates (X), 125 of which are dichotomous. Roughly 20% of the observations in each sample are "treatment" and the rest are "control" units. i am doing propensity score matching, i have already estimated propensity scores(predicted

Dear All, I would like to find something ( references, code,..) to implement a comparison of three treatments in an observational study using the 'Propensity Score'. Any help is much appreciated. Thanks! Giovanni -- dr. Giovanni Parrinello Department of Biotecnologies Medical Statistics Unit University of Brescia Viale Europa, 11 25123 Brescia email: parrinel at med.unibs.it Phone:

There may be benefits to having a machine learning method that explicitly targets covariate balance. We have experimented with optimizing the weights directly to obtain the best covariate balance, but got some strange solutions for simple cases that made us wary of such methods. Machine learning methods that yield calibrated probability estimates should do well (e.g. those that optimize the

Dear List, I am having difficulty running the ps() function when variables are stored as factors and was hoping someone could provide some advice on how to proceed. I am running propensity score matching as outlined in: Greg Ridgeway, Dan McCarey, Andrew Morral, Lane Burgette and Beth Ann Grin (May 3, 2013) Toolkit for Weighting and Analysis of Nonequivalent Groups: A tutorial for the twang

I'm using the "Match" package to do propensity score matching. Here's some example code that shows the problem that I'm having (much of this code is taken from the Match package documentation): *data(lalonde) glm1 <- glm(treat~age + I(age^2) + educ + I(educ^2) + black + hisp + married + nodegr + re74 + I(re74^2) + re75 + I(re75^2) + u74 + u75,

Hola chic en s, alguien con experiencia en propensión score matching? Planteo duda: Clasicamente el PSM se ha utilizado en un intento de homogeneizar cohortes de enfermos quienes han estado ?expuestos? a un tratamiento x Vs aquellos que no han estado expuestos (no expuestos). Esto aplica para medicamentos o procedimientos quirúrgicos o no. Bien, En algún articulo he leído que el PSM se puede

Hello , My optmatch package is loaded on R and otherwise running fine. I get an error after lcds successfully completes a logistic regression and I then try to obtain a propensity score: pdist <- pscore.dist(lcds) Error: could not find function "pscore.dist" Does anyone know if pscore.dist is part of the optmatch package, or is it contained in another package that I need to

"Matching" version 0.48 is now available on CRAN. Matching provides functions for estimating causal effects by multivariate and propensity score matching. The package includes a variety of univariate and multivariate tests to determine if balance has been obtained by the matching procedure. These tests can also be used to determine if an experiment or quasi-experiment is balanced on

"Matching" version 0.48 is now available on CRAN. Matching provides functions for estimating causal effects by multivariate and propensity score matching. The package includes a variety of univariate and multivariate tests to determine if balance has been obtained by the matching procedure. These tests can also be used to determine if an experiment or quasi-experiment is balanced on

Hello My optmatch package is loaded and otherwise running fine. I get an error after lcds successfully completes logistic regression and I'm trying to obtain a propensity score: > pdist <- pscore.dist(lcds) Error: could not find function "pscore.dist" I searched the help files, other online sources, could find no answer for this. Any advice would be greatly appreciated!

Hello! If I understand this listserve correctly, I can email this address to get help when I am struggling with code. If this is inaccurate, please let me know, and I will unsubscribe. I have been struggling with the same error message for a while, and I can't seem to get past it. Here is the issue: I am using a data set that uses -1:-9 to indicate various kinds of missing data. I changed

Hola chic en s, alguien con experiencia en propensión score matching? Planteo duda: Clasicamente el PSM se ha utilizado en un intento de homogeneizar cohortes de enfermos quienes han estado ?expuestos? a un tratamiento x Vs aquellos que no han estado expuestos (no expuestos). Esto aplica para medicamentos o procedimientos quirúrgicos o no. Bien, En algún articulo he leído que el PSM se puede

Hi. I am using the Matching package for propensity score matching. For each treated unit, I want to find all control units whose propensity scores lie within a certain distance from the treated unit. The sample code is as follows: > library(Matching) > x <- rnorm(100000) > y <- rnorm(100000) > z <- rbinom(100000,1,0.002) > logit.reg <-

Hello all. I am doing one part of an evaluation of a mandatory welfare-to-work programme in the UK. As with all evaluations, the problem is to determine what would have happened if the initiative had not taken place. In our case, we have a number of pilot areas and no possibility of random assignment. Therefore we have been given control areas. My problem is to select for survey individuals in

The Toolkit for Weighting and Analysis of Nonequivalent Groups (twang 1.0) has been released to CRAN. The package collects functions useful for computing propensity score weights for treatment effect estimation, developing nonresponse weights, and diagnosing the quality of those weights. The package includes a vignette containing some basic theory and walks through two examples. It is available by

I have to the treatment effect base on the observational data.And the treatment variable is multinomial rather than binary.Because the treatment assignment is not random,so the selection-bias exists.Under this condition,what's the best way to estimate the treatment effect? I know that if the treatment is binary,I can use propensity score matching using MatchIt package.But what about

Hi! I'm trying to perform propensity score matching on survey data and so for each individual observation I have a statistical weight attached. My question is: is there a way within the package to consider these weights in the matching procedure? Thank you very much. -- Maria Cristina Maurizio [[alternative HTML version deleted]]

Dear list, I am trying to set up a propensity-weighted regression using the survey package. Most of my population is sampled with a sampling probability of one (that is, I have the full population). However, for a subset of the data I have only a 50% sample of the full population. In previous work on the data, I analyzed these data using SAS and STATA. In those packages I used a propensity weight