From: Frank E Harrell Jr <f.harrell_at_vanderbilt.edu>

Date: Wed 06 Apr 2005 - 05:42:41 EST

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> i want a rule inspired by non-overlap in propensity score space, but that

*> binds in the space of the Xs. because i don't really know how to
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*> interpret the fact that i've excluded, say, people with scores > .87,
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*> but i DO know what it means to say that i've excluded people from
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*> country XYZ over age Q because i can't find good matches for them. if i
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*> make my rule based on Xs, i know who i can and cannot make inference for,
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*> and i can explain to other people who are the units that i can and cannot
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*> make inference for.
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*>
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*> after posting to the list last night, i thought of using the RGENOUD
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*> package (genetic algorithm) to search over the space of exclusion rules
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*> (eg., var 1 = 1, var 2 = 0 var 3 = 1 or 0, var 4 = 0); the loss function
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*> associated with a rule should be increasing in # of tr units w/out support
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*> excluded and decreasing in # of tr units w/ support excluded.
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*>
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*> it might be tricky to get the right loss function, and i know this idea is
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*> kind of nutty, but it's the only automated search method i could think of.
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*>
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*> any comments?
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*>
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*> alexis
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> On Tue, 5 Apr 2005, Frank E Harrell Jr wrote:

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Date: Wed 06 Apr 2005 - 05:42:41 EST

Alexis J. Diamond wrote:

*> hi,
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> thanks for the reply to my query about exclusion rules for propensity

*> score matching.
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>>Exclusion can be based on the non-overlap regions from the propensity. >>It should not be done in the individual covariate space.

> i want a rule inspired by non-overlap in propensity score space, but that

Use the X space directly will not result in optimum exclusions unless you use a distance function but that will make assumptions. My advice is to use rpart to make a classification rule that approximates the exclusion criteria to some desired degree of accuracy. I.e. use rpart to predict propensity < lower cutoff and separately to predict propensity > upper cutoff. This just assists in interpretation.

Frank

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>>I tend to look >>at the 10th smallest and largest values of propensity for each of the >>two treatment groups for making the decision. You will need to exclude >>non-overlap regions whether you use matching or covariate adjustment of >>propensity but covariate adjustment (using e.g. regression splines in >>the logit of propensity) is often a better approach once you've been >>careful about non-overlap. >> >>Frank Harrell

> On Tue, 5 Apr 2005, Frank E Harrell Jr wrote:

>>adiamond@fas.harvard.edu wrote: >> >>>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 probabilities) using logistic regression, and in each sample i >>>am going to have to exclude approximately 100 treated observations for which I >>>cannot find matching control observations (because the scores for these treated >>>units are outside the support of the scores for control units). >>> >>>in each sample, i must identify an exclusion rule that is interpretable on the >>>scale of the X's that excludes these unmatchable treated observations and >>>excludes as FEW of the remaining treated observations as possible. >>>(the reason is that i want to be able to explain, in terms of the Xs, who the >>>individuals are that I making causal inference about.) >>> >>>i've tried some simple stuff over the past few days and nothing's worked. >>>is there an R-package or algorithm, or even estimation strategy that anyone >>>could recommend? >>>(i am really hoping so!) >>> >>>thank you, >>> >>>alexis diamond >>> >> >> >> >>-- >>Frank E Harrell Jr Professor and Chair School of Medicine >> Department of Biostatistics Vanderbilt University >>

-- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.htmlReceived on Wed Apr 06 05:52:08 2005

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