similar to: Method for selection bias with multinomial treatment

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

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 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:

Hi there, I'm wondering what Zelig in the following situation (code below) actually does. Is this considered as a so called regression adjustment after the propensity score matching? library(MatchIt) library(Zelig) data(lalonde) re78 represents the outcome variable 1. With Zelig m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde)

Hello, I am trying to use R for propensity score matching in SPSS.? I have version 21 of SPSS and I downloaded R 2.14.2 as directed as well as the R Essentials plug-in.? I have run a test for R and it appears to be running correctly.? I then downloaded psmatching3 and have tried to use the PS matching dialog in SPSS.? However, I continue to run into problems as SPSS reports that there is no

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 looking to built a simple example of a very basic propensity score adjustment, just using the estimated propensity scores as inverse probability weights (respectively 1-estimated weights for the non-treated). As far as i understood, MLE predictions of a logit model can directly be used as to estimates of the propensity score. I already considered the twang package and the

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

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 <-

look at dev.new() to specify plot window size and then ?layout to specify number and size of each plot in the window Jiaqi.Zhang wrote > Hi, all, > > I am working on the following code to learn how to plot graphs together. I > used the par(mfrow=c(1,3)) function to try to put all three plot() graphs > together. But it always fail without any error message? Can anybody help

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

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

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

Hi! I am running a simulation testing group balance using different propensity score matching techniques. I am using the MatchIt package to match and analyze the data. Since I am running this analysis many, many times with different simulated data sets, I need to make the output from the MatchIt summary and put it into a dataset and then append each successive run to the end of that dataset.

Dear R users, I am trying to estimate to estimate the average treatmen effect on the treated (ATT) using first the MatchIt software to weight the data set and, after this, the Zelig software as shown in Ho et al. (2007). See here for an explanation of how to apply this technique in R: http://imai.princeton.edu/research/files/matchit.pdf I encounter a slight problem when I apply the weights that

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 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!

I have several phones running Cisco's 6.0 SIP software release at this time. Two of the phones have not shown any abnormal behaviors, but one of them has an unsettling propensity to lock up after several hours, where the softkey labels disappear and the phone stops registering, requiring the standard *-6-settings reboot sequence. Otherwise, the phone seems to work OK except for a slight

Dear List, I have a theoretical question related to epidemiological data analysis: If the treatment status (tx = 0,1) changes over time for the patients in a non-randomized cohort, is there a way to estimate the treatment effect? (i.e., after joining the study, some patients may have to wait for a period of time before receiving the treatment, i.e., the situation of patient with id == 2 for the

Hi I have a question (which may be an obvious one). It is about an idiom which I have seen quite often: o <- order(x); <- x[o] vs. the alternative x <- sort(x) I am just wondering as to the rationale behind the order/reindex idiom vs sorting. Especially as there seems to be a marked performance difference (especially for integer vectors): > x <- trunc(runif(1E6, 1, 100)) >