David Newman, Ph.D. Florida Atlantic University Christine E. Lynn College of Nursing

1. An Illustration of How to Use Regression-Discontinuity Designs in Medical Settings: An Alternative to True Experimental Design in Testing Intervention and Treatment Effectiveness David Newman, Ph.D. Florida Atlantic University Christine E. Lynn College of Nursing Isadore Newman, Ph.D. Florida International University, Adjunct Prof. College of Medicine

2. Ethical Concerns with Experimental Research • True Experimental Design and Randomized Drug Trials • A recurring control group is difficult • Hospitals tend to implement protocols as a whole unit or at least on specific floors eliminating the possibility of a control group • If the suspected TX is better and is most likely going to save lives we are obligated to give our patients the best TX available.

3. What is Regression Discontinuity Analysis (RD) • Powerful methodological option to experimental design when one is not feasible • Traditional RD tests slope difference between control and TX groups • Alternative RD Approach examines changes in individuals over time. • This can be used to enhance evidence based practice and intervention in clinical settings.

4. RD Traditional Vs Alternative • RD tests pre and post slope differences between the group regression lines at the intervention point for statistical significance. Alternative Traditional Pain where YPost = post measures Z = group assignment (0 = control; 1 TX) XPre = pre measures b’s = estimated sample regression weights e= residual error Baseline After TX

5. Traditional RD Steps • Step 1: Create precut score = Pre- Xpre (Orthogonal) can also use theoretical cut score • Step 2: Create interaction term group*precut. • Step 3: Test: Post = Intercept + group + interact + precut • Step 4: Test: Post = intercept + group + precut • Step 5: Compare separate group results

6. Alternative RD Approach Steps • Step 1: Create Person Vectors (1 if score belongs to personi, 0 otherwise) • Step 2: Create Time by TX (Before and after) interaction

7. Alternative RD Approach Steps (Cntd) • Step 3: Test before and after TX slope differences Restricted (R21): Y = acUc + acXc + Zb1(P1) +…Zbn(Pn) + e1 Full (R22): Y= a01U1 + a1X1 + a02 U2 + a2X2 + Zb1(P1) +…Zbn(Pn) + e1 Ho:a1 = a2 = ac (common slope) • Step 4: Test before and after TX Intercept differences Restricted (R21): Y = a0U1+ aX1+ a2X2 +Zb1(P1)+…Zbn(Pn) +e2 Full (R22): Y= a01U1 + a1X1 + a02 U2 + a2X2 + Zb1(P1)+…Zbn(Pn) + e1 Ho: a01 = a02 = a0 (common intercept) • Step 5: Compare separate group results

8. Descriptive Results • From the descriptive statistics and the linear graph with projected slopes it appears that the treatment did have a large impact on pain reduction. • But were there significant changes over time? And was there a significant immediate impact?

9. Results TX Effect on Slope • This indicates that there was a significant decrease in patients change in pain over time after the initiation of the treatment.

10. Results TX Effect on Slopes • This indicates that there was a significant decrease in patients’ pain after the initiation of the treatment (Intercept).

11. Advantages of Using The Alternative RD • With the Person Vectors we can: • Using the partial regression weight and the significance values in the coefficients table, we can ID the individuals that had significant TX reactions and those that did not. • Then code those people as successful or not ( 1, 0) • See if demographics predict the likelihood of TX success. • Modify TX so that it best fits the patient • Variables from Theories, • Focus Groups • Qualitative analysis or Q-Factor Analysis

12. Future Methodology Development • Additional Analysis that can be used with RD • If the criterion variable was the amount of time it took the patient to reduce pain to his or her subjective acceptable level, then Survival Analysis can be used. • HLM is another method I personally use to test RD. However this method does not provide the coefficient table with the Person Vectors and limits further examination of the TX effects. • GEE is akin to HLM but does not rely on the parametric assumptions of the data. Therefore, this technique should be used if the data does not meet the assumptions required for HLM. • With ALL of these, linear and nonlinear trends can be analyzed.

13. References • Bottenberg, R.A. and Ward, J.H. (1963). Applied Multiple Linear Regression. Lackland Air Force Base, San Antonio, TX: Aerospace Medical Division, No. AD413128. • Newman, I. (2012). Designing General Linear Models to Test   Research Hypotheses. University Press of America: Lanham, MD. • Schumacker, R.E. ( 2007). Regression Discontinuity: Examining Model Misspecification. • Multiple Linear Regression Viewpoints, 33(2), 6-10. • Trochim, William M. K. (1984). Research Design for Program Evaluation, the Regression Discontinuity Approach. Sage Publications: Beverly Hills, CA.

14. Contact Information David Newman, PhD Assistant Professor, Statistician Christine E. Lynn College of NursingFlorida Atlantic University777 Glades Rd. Boca Raton, FL 33431-2048Office Phone: 561-297-2670 dnewma14@fau.edu Isadore Newman, PhD Visiting Scholar, Office of Research and Graduate Studies, College of Education, Florida International University Adjunct Professor, Dept. of Human and Molecular Genetics, Herbert Wertheimer College of Medicine, Florida International University 11200 Tamiami Trail, ZEB 310, Miami, FL 33199 Office (305) 348-2975 newmani@fiu.edu