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WHY YOU NEED IT
TWO NON-EQUIVALENT GROUPS
Patients in specialized units
People who attend a fundraising event
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?
ANY TIME YOU CAN ASK THE QUESTION ….
Is there a difference on OUTCOME between levels of “treatment” A, controlling for X, Y and Z ?
ExamplesOUTCOME “TREATMENT”
LEVELSCOVARIATES
DROP OUT PUBLIC, PRIVATE INCOMEPARENT EDUCATIONGR. 8 ACHIEVEMENT
BMI DAILY SOFT DRINKSNO SOFT DRINKS
GENDERAGERACEEXERCISE FREQ.
DEATH LIVES AT HOMENURSING HOME
AGEGENDERTOTAL ER VISITS
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
3. Run logistic regression to generate propensity scores
LOGISTIC REGRESSION VARIABLES dep
/METHOD=ENTER indep1 indep2 indep3
/SAVE=PRED
/CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).
RENAME VARIABLES (PRE_1=propen) .
SAVE OUTFILE= "test.sav" .
4. Select matching method
1. Quintiles
2. Nearest neighbors
3. Calipers
ALL OF THE ABOVE CAN BE DONE EITHER WITH OR WITHOUT REPLACEMENT
Our problem
We have cities with and without specialized care units (trauma center, burn unit)
We want to see if the cities with specialized units have higher survival rates, controlling for other variables
Creating Propensity Scores
What variables are related to group?
Example:
Age group and gender were significantly related to city.
Preparing the data
Maximum likelihood solutions are large sample methods. You may wish to combine or delete categories with small numbers
Consider dropping or combining categories… (this was done)
MECHANISM Frequency Cumulative
Percent Percent
Fall 1370 19.2 19.2
GSW 1357 19.0 38.2
MVC 2161 30.3 30.3 68.5
Other 588 8.2 76.7
Accidents 1077 15.1 91.8
Shark attacks 44 .6 92.4
HWB 542 7.6 100.0
Total 7139 100.0
Start SPSSOpen example.sav
File > Open > Data
Note: This is real data with some changes made for confidentiality
An appearance by Captain Obvious Because propensity score
matching essentially checks that the difference between groups disappears once pre-existing differences are controlled, before you go to all of this trouble, test to see that the groups are ,in fact, significantly different.
Move variables desired to Rows and ColumnsClick on Statistics
Note: You can put multiple variables under rows
SYNTAX
CROSSTABS
/TABLES=OUTCOME Age_groups CategGCS BY City_of_injury
/FORMAT=AVALUE TABLES
/CELLS=COUNT
/COUNT ROUND CELL.
Basic statistics to test covariatesTesting for differences on numeric variables
ANALYZE > COMPARE MEANS > INDEPENDENT SAMPLES T-TEST
What differs between cities?
Age in years, Age group was not significantly different between cities
Gender, Trauma Type, Mechanism of Injury, Admission to ICU, GCS, ISS & RTS are all significantly different between cities
What differs between outcomes? ICU_LOS,Trauma Type, Mechanism of
Injury, Admission to ICU, GCS, ISS & RTS are all significantly different between cities
What variables should be controlled?Example of City A vs B- Logistic regression with city as dependent
and age group, trauma type & admission to ICU as independents.
- Logistic regression with city as dependent and Age Group, Gender, Trauma Type, Mechanism of Injury, Admission to ICU, GCS, ISS & RTS as independents.
Since running the logistic regression and creating propensity scores takes relatively little time it is not much trouble to test more than one model