Download - D6 isaranuwatchai cadth 12_apr16
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YOU ARE
HERE
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THINKING OUTSIDE RCTs
Wanrudee Isaranuwatchai, PhDCADTH Symposium
12 April 2016
Marcus Tan, MDKate Butler
Tony Zhong, MDJeffrey S. Hoch, PhD
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RCTs
How a person-level cost-effectiveness analysis from an observational study can show
“value for money” of a health intervention?
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No conflict of interest that may affect this presentation
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Outline
Observational studies Net benefit regression (NBR) Case study of breast reconstruction procedures Summary
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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
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Net Benefit Regression
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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)
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How to get to INB: Data
NBi = λEi – Ci
Hoch et al. Health Economics, 2002; 11: 415-430
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The regression in NBR – SLR
NBi = β0 + β1(TX)i + εi
β1 = NBTX – NBUC
= ΔNB = INB
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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
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Outline
Observational studies Net benefit regression (NBR) Case study of breast reconstruction procedures Summary
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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
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Objective
To compare the cost and outcome of DIEP flap to MS-TRAM flap in autologous breast reconstruction from the hospital perspective
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Statistical Analysis
Descriptive analysis
Net benefit regression Adjusted for age, chemotherapy, radiation, laterality,
timing, income, and ethnicity
Cost-effectiveness acceptability curve (CEAC)
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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
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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
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CEAC
$0 $1,000 $5,000 $10,000 $50,000 0.0
0.1
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Willingness-to-pay for 1 more unit of outcome
Prob
abili
ty th
at D
IEP
is co
st-e
ffecti
ve
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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
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THANK [email protected]
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delta
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-20 -10 0 10 20delta_e
Bootstrap adjusted delta C and delta E