YOU ARE

HERE

THINKING OUTSIDE RCTs

Wanrudee Isaranuwatchai, PhDCADTH Symposium

12 April 2016

Marcus Tan, MDKate Butler

Tony Zhong, MDJeffrey S. Hoch, PhD

RCTs

How a person-level cost-effectiveness analysis from an observational study can show

“value for money” of a health intervention?

No conflict of interest that may affect this presentation

Outline

Observational studies Net benefit regression (NBR) Case study of breast reconstruction procedures Summary

Observational Studies

Effectiveness Validity Affordability

Lack of randomization Confounding Selection bias …

Concato, Shah, Horwitz. NEJM, 2000; 342; 1887-1892

Propensity score matching Instrumental variable

Multivariate regression

Net Benefit Regression

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)

How to get to INB: Data

NBi = λEi – Ci

Hoch et al. Health Economics, 2002; 11: 415-430

The regression in NBR – SLR

NBi = β0 + β1(TX)i + εi

β1 = NBTX – NBUC

= ΔNB = INB

The regression in NBR – MLR

NBi = β0 + β1(TX)i + βj(X)i,j + εi

β1 = INB = NBTX – NBUC Adjusted for X

Cost-effective: ICER < λ or INB > 0 β1 = INB

p

j = 1

Outline

Observational studies Net benefit regression (NBR) Case study of breast reconstruction procedures Summary

Case Study

Population All women receiving either DIEP or MS-TRAM between 2008 and 2012 in one hospital

Intervention Deep inferior epigastric perforator (DIEP) flaps

Comparator Muscle-sparing transverse rectus abdominis myocutaneous (MS-TRAM) flaps

Outcome Patient-reported satisfaction with outcome (BREAST-Q)

Perspective Hospital

Costs Operating room; hospital staysMedication; allied health careMedical imaging; overhead

Time horizon 2 years

Objective

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

Statistical Analysis

Descriptive analysis

Net benefit regression Adjusted for age, chemotherapy, radiation, laterality,

timing, income, and ethnicity

Cost-effectiveness acceptability curve (CEAC)

Descriptive AnalysisTX (N = 180) UC (N = 47)

Age ± SD 50.3 ± 8.7 52.7 ± 8.6Chemotherapy 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 ± $4,289Satisfaction with outcome ± SD 73.8 ± 27.9 71.0 ± 28.6

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

CEAC

$0 $1,000 $5,000 $10,000 $50,000 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Willingness-to-pay for 1 more unit of outcome

Prob

abili

ty th

at D

IEP

is co

st-e

ffecti

ve

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

THANK YOUIsaranuwatcW@smh.ca

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delta

_c

-20 -10 0 10 20delta_e

Bootstrap adjusted delta C and delta E