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4. THINKING OUTSIDE RCTs Wanrudee Isaranuwatchai, PhD CADTH Symposium 12 April 2016 Marcus Tan, MD Kate Butler Tony Zhong, MD Jeffrey S. Hoch, PhD

5. How a person-level cost-effectiveness analysis from an observational study can show “value for money” of a health intervention?

6.  No conflict of interest that may affect this presentation

7. Outline  Observational studies  Net benefit regression (NBR)  Case study of breast reconstruction procedures  Summary

10. Observational Studies  Effectiveness  Validity  Affordability  Lack of randomization  Confounding  Selection bias …… Concato, Shah, Horwitz. NEJM, 2000; 342; 1887-1892 Propensity score matchingInstrumental variable Multivariate regression

11. Net Benefit Regression

12. Compared to UC, is TX cost-effective?  ICER = ΔC/ΔE  Cost-effective = ICER < λ ΔC/ΔE < λ ΔC < λΔE 0 < λΔE – ΔC  Cost-effective = INB > 0 (= INB)

13. How to get to INB: Data NB EλC NB i = λE i – C i Hoch et al. Health Economics, 2002; 11: 415-430

14. The regression in NBR – SLR  NB i = β 0 + β 1 (TX) i + ε i  β 1 = NB TX – NB UC = ΔNB = INB

15. The regression in NBR – MLR  NB i = β 0 + β 1 (TX) i +  β j (X) i,j + ε i  β 1 = INB = NB TX – NB UC  Adjusted for X  Cost-effective:  ICER 0  β 1 = INB p j = 1

16. Outline  Observational studies  Net benefit regression (NBR)  Case study of breast reconstruction procedures  Summary

17. Case Study PopulationAll women receiving either DIEP or MS-TRAM between 2008 and 2012 in one hospital InterventionDeep inferior epigastric perforator (DIEP) flaps ComparatorMuscle-sparing transverse rectus abdominis myocutaneous (MS-TRAM) flaps OutcomePatient-reported satisfaction with outcome (BREAST-Q) PerspectiveHospital CostsOperating room; hospital stays Medication; allied health care Medical imaging; overhead Time horizon2 years

18. Objective  To compare the cost and outcome of DIEP flap to MS-TRAM flap in autologous breast reconstruction from the hospital perspective

19. Statistical Analysis  Descriptive analysis  Net benefit regression  Adjusted for age, chemotherapy, radiation, laterality, timing, income, and ethnicity  Cost-effectiveness acceptability curve (CEAC)

20. Descriptive Analysis TX (N = 180)UC (N = 47) Age ± SD 50.3 ± 8.752.7 ± 8.6 Chemotherapy 106 (59%)29 (62%) Radiation therapy 89 (49%)30 (64%) Household income Low ($0 - $39,999) Medium ($40K - $99,999) High (≥ $100,000) 23 (13%) 68 (38%) 89 (49%) 7 (15%) 12 (25%) 28 (60%) Ethnicity Caucasian Others 139 (77%) 41 (23%) 40 (85%) 7 (15%) Laterality 101 (56%)16 (34%) Timing 94 (52%)29 (62%) Cost ± SD* $15,344 ± $4,728$16,681 ± $4,289 Satisfaction with outcome ± SD 73.8 ± 27.971.0 ± 28.6

21. Net Benefit Regression Willingness-to-pay (for 1 unit of outcome) INB (Incremental Net Benefit) λ = $0$575 λ = $1,000$2,924 λ = $5,000$12,320 λ = $10,000$24,066 λ = $50,000$118,029  NB from λ of $0 to $50,000 for each patient  Run regression models for each λ

22. CEAC

23. Discussion  Compared to MS-TRAM, DIEP could be an economically attractive option  λ from $0 to $50,000: INB > 0  p(TX=CE) ~ 70%  Limitations  Single site  One perspective  One outcome  Unknown confounders

24. THANK YOU IsaranuwatcW@smh.ca