experience and philosophy of data modeling.2
TRANSCRIPT
1
Views and Experience in Data Modeling
J. Michael Menke, January 1, 2016
After completing courses in health technology assessment for my PhD minor in
pharmacoeconomics, I was invited by faculty in my psychology department and the
Arizona Cancer Centre to build a cost-effectiveness model. The first task was to replicate
current published models that relied on identifying and excising colorectal polypoid
lesions as the primary sequence in cancer genesis. My foundation source was a key study
by Frazier et al (2000),1 a cost-effectiveness model of the impact of screening strategies
and colonoscopy for polyps on saved life-years.
The Frazier parameters then needed updating with the new evidence published since 2000.
The final step was to introduce a variable emerging in the literature as having an
important impact on the colorectal cancer disease burden: the nonpolypoid, or flat, lesion.
Nonpolypoid lesions are relatively rare, but convert to cancer at a much higher rate to a
more aggressive form. Furthermore, flat lesions can be virtually invisible to conventional
optical colonoscopies and sigmoidoscopies. Ultimately, flat lesions might explain more
variance in the gap between predicted and actual cancer cases, screening, and control in
CDC reports of prevalence and death rates.
Using TreeAge and R software, Markov Chain Monte Carlo state compartment modeling
of 62 scenarios was executed based on clinically important thresholds of lesion
1 Frazier AL; Graham A. Colditz GA; Charles S. Fuchs CS; et al. Cost-effectiveness of Screening for Colorectal Cancer in the General Population. JAMA. 2000;284(15):1954-1961 (doi:10.1001/jama.284.15.1954)
2
prevalence, detectability, and expected and reasonable screening rates. Per convention,
clinical probability of success was described in beta distributions and costs by gamma
distributions.
After working for several years on this project, my Arizona advisors moved on and lost
interest, but I resurrected the project later in Malaysia for one of my own PhD students
pursuing health technology assessment.
Since then, I have modeled data in Bayesian indirect comparisons of treatments for
smoking cessation and spinal manipulative therapy, using WinBUGS. Most recently, I
extended Bayesian meta-analysis and data modeling for parameter estimation of accuracy
of biomarkers used in screening for oral cancer.
On the following pages are selected graphics from colorectal screening and other models.
3
Comply
0.800
--- Markov InformationTerm : _stage > 50 & (_stage > 50 | _stage_eff < .001)
[+] $3,937,190.1458 / 43.2346
No screening
0.200
--- Markov InformationTerm : _stage > 50 & (_stage > 50 | _stage_eff < .001)
[+] $941,646.2485 / 46.1750
Frazier + flats$3,338,081.3664 / 43.8227
Comply
0.800
--- Markov InformationTerm : _stage > 50 & (_stage > 50 | _stage_eff < .001)
[+] $3,937,190.1458 / 43.2346
No screening
0.200
--- Markov InformationTerm : _stage > 50 & (_stage > 50 | _stage_eff < .001)
[+] $941,646.2485 / 46.1750
No flats / Frazier$3,338,081.3664 / 43.8227
Comply - flats
0.800
--- Markov InformationTerm : _stage > 50 & (_stage > 50 | _stage_eff < .001)
[+] $5,596,201.1330 / 39.1355
No screening
0.200
--- Markov InformationTerm : _stage > 50 & (_stage > 50 | _stage_eff < .001)
[+] $941,646.2485 / 46.1750
Flats$4,665,290.1561 / 40.5434
Menage=55cCRC_distal_perYear=cDistantCRCcCRC_local_perYear=cLocalCRCcCRC_regional_perYear=cGroup2CRCcFRAZ_CRCdistal_perYear=58300cFRAZ_CRClocal_perYear=22000.cFRAZ_CRCregional_perYear=43900cFRAZ_OC=cFrazierOCdistncFRAZ_OC_polypect=cFRAZ_OC_polypect_DistncFRAZ_UFOBT=38cOC=1012cOC_polypectomy=cPolypecpAdherence=0.5pDetectedFlatRateRemoval=0.80pFemLowHiRiskPolyps=femaleHiRiskAdenomas[age + _stage]pFindingCRCDistalNoOC=0.9pFindingCRCLocalNoOC=0.1pFindingCRCRegionalNoOC=0.50pFindingFlatLesionsNoOC=0.01pFindingHighRiskPolypsNoOC=0.05pFindingLowRiskPolypsNoOC=0.01pFlatsPrev=0.046pFlatsToDeath=0.002pFlatsToLocalCRC=0.147pFlatToRegCRC=0.16pFOBTsensCRC=0.33pFOBTsensPolyps=0.95pFOBTspec=0.95pFollowPosFOBTtoOC=0.90pFRAZDeathDistalCRC=0.566pFRAZDeathLocalCRC=0.002pFRAZDeathRegionalCRC=0.032pFRAZDistalCRCtoDeath=1.566*male_mort[age + _stage]pFRAZdistalPolyps=0.61*pFRAZPrevPolypspFRAZhiRiskPolyps=0.02*pFRAZPrevPolypspFRAZHiRiskPolypsCRC=0.05pFrazierShowUp=0.5pFRAZLocalCRCtoDeath=1.002*male_mort[age + _stage]pFRAZlocCRCtoRegCRC=0.28pFRAZLowHiRiskPolyps=0.02pFRAZMaleLowHiRiskPolyps=maleHiRiskAdenomas[age + _stage]pFRAZOCsensHRpolypsCRC=0.95pFRAZPrevLowRiskPolyps=0.98*pFRAZPrevPolypspFRAZPrevPolyps=0.21pFRAZRegionalToDeath=1.032*male_mort[age + _stage]pFRAZRegtoDistalCRC=0.63pFRAZTransitionToPolyps=0.83*maleHiRiskAdenomas[age + _stage]pFRAZ_OC_low_riskPolyps=0.85pFRAZ_spec_FOBTpolyps=0.97pFRAZ_UFOBTsens_Polyps=0.10pHighRiskPolyps=pFRAZPrevPolyps/2pHiRiskPolypsCRC=0.05pMales=0.50pMissedHiRiskPolyps=0.05pMissedLowPolyps=0.05pOCsensFlats=0.78pOCsensHighRiskPolyps_CRC=0.90pOCsensLowRiskPolyps=0.85pOCspecFlats=0.95pOCspecificityForPolyps=0.99pPrep=0.80
No flats / Frazier : $3,338,081.3664 / 43.8227
Generated by Foxit PDF Creator © Foxit Softwarehttp://www.foxitsoftware.com For evaluation only.
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Cos
t
Effectiveness
CE Scatterplot of Super Converters
C(Frazier)
C(60 / 90 / 0.1%)
C(3. 60 / 60 / 1%)
C(4. 60 / 75 / 0.1%)
C(5. 60 / 90 / 1%)
C(6. 60 / 60 / 0.1%)
C(7. 60 / 75 / 1%)
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1. McKnight model - OC sens to Flats: 0.78, adherence: 0.80
1a. McKnight model - OC sens to flat lesions: 0.78, adherence: 0.60
2. Frazier + flat detection (58%)-death, adhere = .8
3. Comply - Frazier original: flats active, but missed / ignored
4. Frazier original model: flats ignored in model
5. No screen 1 - & no cost of care just VSL
6. No screen 2: Tx starts with 0.5 of local CRC cases-symptomatics
CRC death rate by decade of age
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60 65 70 75 80 85 90
−120
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40% Screening
Range of Sensitivity to NPL
Net
Mon
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60 65 70 75 80 85 90
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Net
Mon
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60 65 70 75 80 85 90
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Net
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etar
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7
Effectiveness
Cost
−100000−50000
050000100000
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1 4 7 11 15 19 23 27 31 35 39 43 47 51 55 59
−150
000
−100
000
−500
000
5000
0
Model(sorted by NMB)
NM
B co
mpa
red
with
no
scre
enin
g
NPL Prevalence01.5369
9
DF MS VarComp PercVar
iter.f 99 8257779585.00 107823728.21 6
scrnRate.f 3 225464831909.61 143646240.83 8
NPLsens.f 2 56695277872.01 23403546.94 1
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iter.f:scrnRate.f 297 529901466.82 29316229.49 2
iter.f:NPLsens.f 198 359079647.10 12228514.49 1
scrnRate.f:NPLsens.f 6 878624984.66 1356042.19 0
iter.f:NPLprev.f 396 1093825457.12 78531038.46 4
scrnRate.f:NPLprev.f 12 8867489426.98 28766472.99 2
NPLsens.f:NPLprev.f 8 8955375902.05 21733692.60 1
iter.f:scrnRate.f:NPLsens.f 594 42997683.16 2043340.64 0
iter.f:scrnRate.f:NPLprev.f 1188 79941321.34 15720113.79 1
iter.f:NPLsens.f:NPLprev.f 792 104292654.19 17877918.55 1
scrnRate.f:NPLsens.f:NPLprev.f 24 190387188.41 1576062.08 0
iter.f:scrnRate.f:NPLsens.f:NPLprev.f 2376 32780979.98 32780979.98 2
1
10