Propensity Score Matching Using SAS Enterprise Guide

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  • Welcome (name, who presentation is for etc), 15 minute presentation. I work in Justice at the Scottish Government but the work for this presentation is not in the name of SG. Originally a part of research for Napier University.
  • A thought – many of you will have been to a particular university or school. Holding your circumstances and background constant, have you wondered what would have been the result if you had attended somewhere else ?
  • Here is the first part of the process flow which does the propensity score. When you click “Run process flow” it provides a dialog box where you choose several options. These have been set up using parameters as macro variables – next slide
  • It shows that I have chosen the Caliper method and a distance of 0.0001 between scores. It will only match if the scores between treatment and control are “this close”
  • The source dataset looks like this. It has 11 variables, a product field and a result field
  • So in PSM the first step is to use SAS Proc Logistic to obtain scores. Then sort the scores.
  • In PSM the next step after scoring, is to match (the scores in the treatments to the scores in the controls).
  • Here is the part of the process flow which does the matching. The parameterised code is used in the “matching macro” SAS code
  • Here are the results. The caliper method with a distance of 0.0001 has found that only 303 out of 980 can be matched between treatment and control.
  • And finally for some examples
  • Reference
  • Propensity Score Matching Using SAS Enterprise Guide

    1. 1. Propensity Score Matching Using SAS Enterprise Guide. Ian Morton Newtyne SAS User Gathering (N-SUG1) Wednesday 19 th May 2010.
    2. 2. What is the problem ? <ul><li>I have a fictitious dataset containing: </li></ul><ul><ul><li>a set of background characteristics (about customers who bought a product); </li></ul></ul><ul><ul><li>an indicator of the year they bought the product (e.g. year 1 or year 2); and </li></ul></ul><ul><ul><li>an outcome (e.g. they bought the product). </li></ul></ul><ul><li>I want an estimate of the difference between whether they bought it or not from one year to the next </li></ul><ul><ul><li>But I want the same or very similar customers; </li></ul></ul><ul><ul><li>I don’t want complications of the “case mix” biasing the answer. </li></ul></ul><ul><li>I will provide real examples later. </li></ul>
    3. 3. How do you do it ? <ul><li>Featured in Allison, P. D. (1999) Logistic Regression Using the SAS System SAS Institute and Wiley, North Carolina. </li></ul><ul><li>Propensity Score Matching - two steps: scoring and then matching. </li></ul><ul><li>Terminology – year 1 (controls), year 2 (treatments) </li></ul>
    4. 4. Process flow – part 1
    5. 5. Parameterised code
    6. 6. SAS dataset <ul><li>Excel, import data, list data and create format </li></ul><ul><li>980 bought in year 1 and 1,020 bought in year 2 </li></ul>
    7. 7. Propensity score <ul><li>Use logistic regression to estimate probability (propensity score) of the treatment </li></ul><ul><li>In SAS (Analyse, regression, logistic) </li></ul><ul><li>Provides a probability (propensity score) for all students; treatment and control. </li></ul><ul><li>The logistic equation is below, where Ti is the treatment status, Xi are the observations and h ( Xi ) is made up of the covariates (age, gender, etc). </li></ul>
    8. 8. Matching <ul><li>Coca-Perraillon, M. (2007) Local and Global Optimal Propensity Score Matching SAS Global Forum 2007, Orlando, Florida, April16th - 19th 2007. </li></ul><ul><li>Need to match treatments to controls by propensity score. </li></ul><ul><li>There are different matching methods </li></ul><ul><li>Once a match has been found for the treatments in the controls, use information on the latter's outcome for inference. </li></ul>
    9. 9. Process flow – part 2
    10. 10. Results <ul><li>980 treatments reduced to 303 treatments </li></ul><ul><li>The controls that match these treatments are: </li></ul><ul><li>The 303 treatments and matched controls have similar characteristics and allow further assessment </li></ul>
    11. 11. Uses of the method <ul><li>Justice </li></ul><ul><ul><li>Want to know if programmes and interventions are successful in reducing reconvictions of offenders over time; </li></ul></ul><ul><ul><li>don’t want complications getting in the way, so need the same offender characteristics between cohorts. </li></ul></ul><ul><ul><li>Example: Ministry of Justice (2010) Evaluating the use of judicial mediation in Employment Tribunals Ministry of Justice Research Series 7/10. </li></ul></ul><ul><li>Medical </li></ul><ul><ul><li>In a case control study need to match cases to controls by say gender, age, smoking status. </li></ul></ul><ul><ul><li>Example: Foster, E. M. (2003) Propensity Score Matching: An Illustrative Analysis of Dose Response. Medical Care 41 10 1183-1192. </li></ul></ul><ul><li>Propensity score matching and then counter-factual inference </li></ul><ul><ul><li>The Scottish Funding Council wanted an estimate of the drop-out rate of students who studied outside Scotland, if they had actually studied in Scotland. </li></ul></ul><ul><ul><li>An estimate of what would have been the outcome if customers who bought product 2 had actually bought product 1. </li></ul></ul><ul><ul><li>Example: Rosenbaum, P. R. and Rubin, D. B. (1983) The central role of the propensity score in observational studies for causal effects Biometrika 70 1 41-56. </li></ul></ul>
    12. 12. Reference <ul><li>Morton, I.D., Penny, K., Ashraf, M.Z. and Duffy, J.C. (In Press) The Use of Propensity Score Matching in Comparing Student Outcomes Sent to Journal of the Operational Research Society </li></ul>

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