search for: estimand

...ry("Matching") data("lalonde") attach(lalonde) names(lalonde) Y <- lalonde$re78 Tr <- lalonde$treat glm1 <- glm(Tr~age+educ+black+hisp+married+nodegr+re74+re75,family=binomial,data=lalonde) pscore.predicted <- predict(glm1) rr1 <- Match(Y=Y,Tr=Tr,X=glm1$fitted,estimand="ATT", M=1,ties=TRUE,replace=TRUE) summary(rr1) > summary(rr1) Estimate...  2624.3  AI SE......  802.19  T-stat.....  3.2714  p.val......  0.0010702  Original number of observations..............  445  Original number of treated obs...............  185  Matched number of observatio...

...600 dependent packages this is not something to take lightly, and I am currently undecided about the best way to go about it.? I'm looking for advice. The change: 20+ years ago I had decided not to include the initial x=0,y=1 data point in the survfit object itself.? It was not formally an estimand and the plot/points/lines etc routines could add this on themselves.? That turns out to have been a mistake, and has led to a steady proliferation of extra bits as I realized that the time axis doesn't always start at 0, and later (with multi state) that y does not always start at 1 (though...

...ing to >> take lightly, and I am currently undecided about the best way to go about it. I'm looking >> for advice. >> >> The change: 20+ years ago I had decided not to include the initial x=0,y=1 data point in >> the survfit object itself. It was not formally an estimand and the plot/points/lines etc >> routines could add this on themselves. That turns out to have been a mistake, and has led >> to a steady proliferation of extra bits as I realized that the time axis doesn't always >> start at 0, and later (with multi state) that y does not al...

...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 baseline covariates. The functions provide valid standard errors and allow one to estimate various estimands. For documentation and further details see: http://jsekhon.fas.harvard.edu/matching Cheers, Jas. ====================================== Jasjeet S. Sekhon Associate Professor Harvard University Center for Basic Research in the Social Sciences jasjeet_sekhon at harvard.edu http://jsekhon.fas....

Dear all, I am trying to program an estimator which maximizes a likelihood type objective function which is basically just lots of sums of indicator functions of data and parameters. In order to make the optimization I would like to smooth these functions. Since they are either 0 or 1, one possibility is to use the normal cdf. I am wondering whether anyone is aware of a less arbitrary choice

...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 baseline covariates. The functions provide valid standard errors and allow one to estimate various estimands. For documentation and further details see: http://jsekhon.fas.harvard.edu/matching Cheers, Jas. ====================================== Jasjeet S. Sekhon Associate Professor Harvard University Center for Basic Research in the Social Sciences jasjeet_sekhon at harvard.edu http://jsekhon.fas....

...nodegr + re74 + I(re74^2) + re75 + I(re75^2) + u74 + u75, family=binomial, data=lalonde) X <- glm1$fitted Y <- lalonde$re78 Tr <- lalonde$treat # one-to-one matching with replacement (the "M=1" option). # Estimating the treatment effect on the treated (the "estimand" option defaults to ATT). rr <- Match(Y=Y, Tr=Tr, X=X, M=1);* And here's where the 'problem' occurs: *summary(rr) # gives an estimate of 2153.3 mean(rr$mdata$Y[rr$index.treated])-mean(rr$mdata$Y[rr$index.control]) # gives an estimate of 1083.848 * Notice that when I simply...

...es this is not something to > take lightly, and I am currently undecided about the best way to go about it. I'm looking > for advice. > > The change: 20+ years ago I had decided not to include the initial x=0,y=1 data point in > the survfit object itself. It was not formally an estimand and the plot/points/lines etc > routines could add this on themselves. That turns out to have been a mistake, and has led > to a steady proliferation of extra bits as I realized that the time axis doesn't always > start at 0, and later (with multi state) that y does not always start a...

...take lightly, and I am currently undecided about the best way to go about it. I'm looking >>> for advice. >>> >>> The change: 20+ years ago I had decided not to include the initial x=0,y=1 data point in >>> the survfit object itself. It was not formally an estimand and the plot/points/lines etc >>> routines could add this on themselves. That turns out to have been a mistake, and has led >>> to a steady proliferation of extra bits as I realized that the time axis doesn't always >>> start at 0, and later (with multi state) that y...

...etailed case study is in http://biostat.mc.vanderbilt.edu/CourseBios330 Chapter 15. Ordinal regression is now a direct competitor to linear models, and far more robust, even allowing spikes in the distribution of Y. * More generality and ease of obtaining bootstrap confidence limits for all estimands (predictions, contrasts). The basic bootstrap is now implemented and tends to work better than the percentile bootstrap in terms of confidence coverage. * Change of Surv to Srv * Added tk/tcl progress bars for bootstrap and other repeated calculations. This can be turned off by specif...