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X 11 X 12 X 13 X 21 X 22 X 23 X 31 X 32 X 33. Research Question.

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**X 11 X 12 X 13**X21 X22 X23 X31 X32 X33**Research Question**• Are nursing homes dangerous for seniors? Does admittance to a nursing home increase risk of death in adults over 65 years of age when controlling for age, gender, race, and number of emergency room visits?**Propensity Score MatchingorDo nursing homes kill you?**ANNMARIA DE MARS, PH.D. & CHELSEA HEAVEN THE JULIA GROUP**WHY YOU NEED IT**TWO NON-EQUIVALENT GROUPS Patients in specialized units People who attend a fundraising event**Any time you can ask the question ….**Is there a difference on OUTCOME between levels of “treatment” A, controlling for X, Y and Z ?**1. Make sure there are pre-existing differences**(Thank you, Captain Obvious)**2a. Decide on covariates**• Are the differences pre-existing or could they possibly be due to the different “treatment” levels? • Race and gender are good choices for covariates. If more students at private vs public schools are black or female, the schooling probably didn’t cause that • Differences in grade 10 math scores may be a result of the type of school**2b. Decide on covariates**Don’t use your outcome variable as one of your covariates**3. Run logistic regression to generate propensity scores**PROC LOGISTIC DATA= datasetname ; CLASS categorical variables ; MODEL dependent = list-of-covariates ; OUTPUT OUT = newdataset PREDICTED= propensity-score;**4. Select matching method**• Quintiles • Nearest neighbors • Calipers ALL OF THE ABOVE CAN BE DONE EITHER WITH OR WITHOUT REPLACEMENT**5. Run matching program & test its effectiveness**6. Run your analysis using the matched data set**An actual example**Do nursing homes kill you?**Our data**Kaiser Permanente Study of the Oldest Old, 1971-1979 and 1980-1988: [California] DEPENDENT VARIABLE: Dthflag = 1 if Died during study period 0 if alive at end of study period**Our data**TREATMENT VARIABLE athome = 1 if lived at home continuously 0 if admitted to nursing home any time during study period**Covariates ***• AGE • RACE • GENDER • TOTAL Emergency Room VISITS ** * Three out of four were DEFINITELY pre-existing differences ** Proxy for health**Create propensity scores**PROC LOGISTIC PROC LOGISTIC DATA= saslib.old ; CLASS athome race sex ; MODEL athome = race sex age_comp vissum1; OUTPUT OUT =study.allpropen PREDICTED = prob; NOTE: No DESCENDING option**QUINTILE MATCHING**EXAMPLE ONE**Part on creating quintiles blatantly copied (almost)**http://www.pauldickman.com/teaching/sas/quintiles.php**Calculate Quintile Cutpoints**PROC UNIVARIATE DATA= saslib.allpropen; VAR prob; OUTPUT OUT=quintile PCTLPTS=20 40 60 80 PCTLPRE=pct; Remember the dataset we created with the predicted probabilities saved in it?**PROC UNIVARIATE**VAR prob; *** predicted probability as variable OUTPUT OUT=quintile PCTLPTS=20 40 60 80 PCTLPRE=pct; *** output to a dataset named quintile, *** create four variables at these percentiles *** with the prefix pct ;**/* write the quintiles to macro variables */**data _null_ ; set quintile; call symput('q1',pct20) ; call symput('q2',pct40) ; call symput('q3',pct60) ; call symput('q4',pct80) ; Just because I am too lazy to write down the percentiles**Create quintiles**data STUDY.AllPropen; set STUDY.AllPropen ; if prob =. then quintile = .; else if prob le &q1 then quintile=1; else if prob le &q2 then quintile=2; else if prob le &q3 then quintile=3; else if prob le &q4 then quintile=4; else quintile=5;**The matching part**Try to control your excitement**Create case & control data sets**DATA small large ; SET study.allpropen ; IF athome = 0 THEN OUTPUT small ; ELSE IF athome = 1 THEN OUTPUT large ;**Create data set of sampling percentages**PROC FREQ DATA = small ; quintile / OUT = samp_pct ;**Create data set of sampling percentages**PROC FREQ DATA = small ; quintile / OUT = samp_pct ;**Create sampling data set**DATA samp_pct ; SET samp_pct ; _NSIZE_ = 1 ; _NSIZE_ = _NSIZE_ * COUNT ; DROP PERCENT ; Just here to make it easy to modify**PROC SURVEYSELECT**SAMPSIZE= input data set can provide stratum sample sizes in the _NSIZE_ variable STRATA groups should appear in the same order in the secondary data set as in the DATA= data set.**SELECT RANDOM SAMPLE**PROC SORT DATA = large ; BY quintile ; PROC SURVEYSELECT DATA= large SAMPSIZE = samp_pct OUT = largesamp ; STRATA quintile ;**Concatenate data sets**DATA study.psm_sample ; SET largesamp small ;**Did it work?**** P <.01 **** P < .0001**Before odds ratio 6.5 : 1**AFTER ODDS RATIO = 3.7: 1

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