2015 ISPOR IMS
CDM user forum
November 08, 2015
Agenda
Item
2
Presenter
Time (min)
Welcome
Mark Lamotte
14.00
IMS CDM Version 9.0:
• Hypoglycemia module + impact
• New type 2 risk equations
• Makeover of type 1 data inputs
• NICE comments on 8.5 and how they were adressed
Volker Foos
Volker Foos
Phil McEwan
Mafalda Ramos
14.10
Pending developments
• New user interface
• The Reference Manual
• User access
Mark Lamotte
Robert
Chomuntowski
15.25
Coffee break
All
15.40
Practical guidance in the use of the model
• The choice for a web based patient simulation
• Random walk and number of patients/iterations
• Impact of BMI on study results
• Sense and nonsense of certain sensitivity analysis
Volker Foos
Phil McEwan
Matthew Madin
Eleonora Lovato
Questions/comments/feedback?
ALL
IMS Health Confidential
15.55
16.45
The IMS Core Diabetes Model Team
3
Scientific lead
Mark Lamotte – mlamotte@be.imshealth.com
Volker Foos – vfoos@ch.imshealth.com
Commercial
development
Adam Collier – acollier@uk.imshealth.com
Mike Gains – mgains@uk.imshealth.com
External advisor
Phil McEwan – phil.mcewan@heor.co.uk
IT
Robert Chomuntowski – RChomuntowski@pl.imshealth.com
Training and model
development
Eleonora Lovato – ELovato@uk.imshealth.com
Mafalda Ramos – mafalda.ramos@be.imshealth.com
Matthew Madin-Warburton – MMadinWarburton@uk.imshealth.com
IMS Health Confidential
Abstracts/Presentation at ISPOR Milano 2015
Current research with the CDM
Monday
RESEARCH
PODIUMS – II
10 November 2015
CE1
15:45
Basal Insulin Regimens: Systematic Review, Network Meta-Analysis and Cost–Utility
Analysis for the National Institute for Health and Care Excellence (NICE) Clinical Guideline
on Type 1 Diabetes Mellitus in Adults (Dawoud et al)
Wednesday
SESSION V
11 November 2015
Author Discussion
Hour
4
PRM72
12:45-13:45
CONTRASTING PREDICTIONS OF CARDIOVASCULAR INCIDENCE DERIVED FROM
ALTERNATIVE RISK PREDICTION MODELS IN TYPE 1 DIABETES
PRM74
12:45-13:45
CONTRASTING MODEL PREDICTED LIFE EXPECTANCY IN PATIENTS WITH TYPE 2
DIABETES ACROSS DIFFERENT MORTALITY RISK PREDICTION MODELS VERSUS
DATA FROM THE CANADIAN CHRONIC DISEASE SURVEILLANCE SYSTEM
PRM84
12:45-13:45
THE IMPORTANCE OF ACCOUNTING FOR BASELINE HYPOGLYCAEMIA
FREQUENCY WHEN MODELLING HYPOGLYCAEMIA DISUTILITY IN TYPE 1
DIABETES MELLITUS
PRM85
12:45-13:45
VALIDATING APPROACHES TO MODELLING END-STAGE RENAL DISEASE USING
THE IMS CORE DIABETES MODEL
PRM88
12:45-13:45
THE IMPACT OF BASELINE HBA1C AND HBA1C TRAJECTORIES ON TIME TO
THERAPY ESCALATION IN TYPE 2 DIABETES MELLITUS
PRM98
12:45-13:45
INVESTIGATING THE IMPACT OF CONTEMPORARY RISK FACTORS FOR DIABETES
COMPLICATIONS AND THEIR EVOLUTION ON RISK PREDICTION USING THE UKPDS
82 EQUATION
PRM111
12:45-13:45
THE ROLE OF PATIENT LEVEL DATA IN ASSESSING HEALTH ECONOMIC VALUE: A
CASE STUDY USING EDGE AND THE CORE DIABETES MODEL
IMS Health Confidential
CDM Version 9.0
The IMS CORE Diabetes Model
Update to CDM v9.0
Scientific updates
• Hypoglycemia module + impact
• New cardiovascular risk equations
• Type 1 update
– Microvascular disease (EDIC)
– Macrovascular (new T1D CV REs)
– Mortality (T1D mortality RE)
• NICE comments on 8.5 and how they were update in the T1D section
of the 9.0 model
6
IMS Health Confidential
Hypoglycemia Module
The Hypoglycemia Module
Developed to more precisely capture the economic impact of hypoglycemia
Impact of events on:
Direct cost
Indirect cots
QALE disutility
Mortality
CDM v8.5
CDM v9.0
diurnal
Daily
NSHE
nocturnal
Mild
Daily
diurnal
SHE1
Daily
nocturnal
Severe
4 month
diurnal
4 month
SHE2
nocturnal
8
IMS Health Confidential
Event Rates
Treatment setting
CDM v8.5
CDM v9.0
diurnal
Daily
NSHE
nocturnal
Mild
Daily
diurnal
SHE1
Daily
nocturnal
Severe
4 month
diurnal
4 month
SHE2
nocturnal
9
IMS Health Confidential
Event Rates (contd.)
Log linear regression equations
NSHE – Log-linear regression model1
Coefficient
Intercept
Baseline age
Baseline HbA1c
HbA1c reduction
Duration of diabetes
% allowed SU
Basal analog used
14.771
-0.088
-0.667
0.427
0.189
0.007
-0.545
SE
Z
1.740
0.021
0.148
0.143
0.035
0.002
0.175
8.49
-4.11
-4.50
2.98
5.33
3.57
-3.11
P(>|z|)
<0.001
<0.001
<0.001
<0.01
<0.001
<0.001
0.002
CDM v9.0
diurnal
Daily
NSHE
nocturnal
SHE – Log-linear regression model1
Coefficient
Intercept
Baseline age
Duration of diabetes
Baseline HbA1c
HbA1c reduction
Biphasic Insuin
10.794
-0.101
0.163
-0.723
0.638
0.768
SE
Z
2.036
0.025
0.039
0.173
0.166
0.312
5.30
-4.12
4.21
-4.17
3.85
2.44
P(>|z|)
<0.001
<0.001
<0.001
<0.001
<0.001
0.015
diurnal
SHE1
Daily
nocturnal
diurnal
4 month
SHE2
nocturnal
1McEwan
et al. Predicting the frequency of severe and non-severe hypoglycaemia in insulin treated type-2 diabetes subjects. Presented at the ISPOR 16th Annual
European Congress, Dublin 2-6 November 2013
10
IMS Health Confidential
Diminishing Disutility for NSHE
Diminishing Disutility for NSHE
• Two independent studies predicting the
same trend of diminishing NSHE annual
disutility
Per event disutility (static vs. dim.)
0.012
Per-event
Diminishing marginal effects
0.009
Per- event disutility
• Overall assumption is that per event annual
utility impact decreases with increasing
number of annual NSHE
0.006
0.003
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
– Large TTO study of >8000 respondents from
five countries (UK, USA, Canada, Germany
and Sweden) (1)
NSHE event rate
Consistency between two distinct studies
0.015
Lauridsen et al.
Per- event disutility
– Data from postal survey of 1305 respondents
from the UK (2)
Currie et al.
0.012
0.009
0.006
0.003
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
NSHE event rate
1Lauridsen
JT et al. Diminishing marginal disutility of hypoglycaemic events: results from a time trade-off survey in five countries..Qual Life Res. 2014
Nov;23(9):2645-50
2Currie et al. Multivariate models of health related utility and the fear of hypoglycaemia in people with diabetes, CMRO Vol. 22, No., 2006, 1523–1534
11
IMS Health Confidential
Diminishing Disutility
Research Abstract 1 (ISPOR Montreal 2014) – To show implications
1) Overall annual decline in QALE
2) QALE gain for a 50% reduction of NSHE
2) QALE gain per 1 event avoided
Conclusions
Nonlinear (diminishing) models produced higher
overall and incremental utility scores for 1-4
NSHE/year and considerably lower scores for
>=5 NSHE/year
1Foos
et al. Illustrating the relationship between the number of hypoglycemic events, event rate reduction and the impact on estimates of quality of life
improvement in health economic studies. ISPOR 19th Annual International Meeting, Montreal, 31 May-June 4, 2014 | PDB80
12
IMS Health Confidential
Diminishing Disutility (contd.)
Research Abstract 2 (EASD 2015) – Case Study
Objectives
• Use CDM to project published real-world audit data for patients with type 1
diabetes switching to insulin degludec (ID) from either insulin glargine or
detemir (IGD) over lifetime using static vs. diminishing approach
• NSHE rate pre switch: 3.9 events/week (203 events/yr)
• NSHE rate post switch: 0.36 events/week (18 events/yr)
1Evans
et al. Insulin degludec early clinical experience: does the promise from the clinical trials translate into clinical practice—a case-based evaluation. Journal of
Medical Economics 2014, 1–10
13
IMS Health Confidential
Diminishing Disutility (contd.)
Research Abstract 2 (EASD 2015) – Case Study
Findings
0.000
• Use of a static disutility results in
negative QALE predictions
-0.038
Static
-0.600
Diminishing
-0.800
Conclusion
-1.000
• When evaluating treatments
associated with high rates of
hypoglycemia, the static disutility
approach may lead to misleading
estimates of QALE that lack face
validity
-1.200
1Lovato
-0.094
-0.400
Disutility
• Baseline utility for diabetes
without complications is 0.785
-0.086
-0.200
-1.056
IGD
ID
E et al. The importance of appropriately incorporating the effects of hypoglycaemia within a health economic model when hypoglycaemia rates are high.
EASD, 14-18 September 2015, Stockholm, Sweden
14
IMS Health Confidential
New Cardiovascular risk equations
New CV risk prediction models
Background
• In 2012 the Scottish Medicines Consortium (SMC) raised concerns
regarding the appropriateness of using UKPDS risk equations
Question
• Can contemporary diabetes management strategies (including DPP4’s, GLP1’s, SGLT2’s etc.) be compared in decision analytic models
that are based on evidence from the UKPDS?
Rationale
• UKPDS out of date
• Study was conducted in the 1990
• Patients managed differently (intensive treatment with SU, insulin,
MET, combinations)
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IMS Health Confidential
New CV risk prediction models
IMS research1 to inform this question
UKPDS
Summary
0.0
• Compared to UKPDS
REs (OM & risk
engines)
B) To predict the CV risk
reduction associated
with intervention vs.
control
1McEwan
2Van
17
0.1
0.2
0.3
0.00
Risk equation
0.0
0.1
0.2
0.02
0.04
0.06
0.08
ADDITION CV risk reduction
Framingham (Stroke) 1991
UKPDS 60 (Stroke) 2002
UKPDS 68 Stroke 2004
Hong Kong (Stroke) 2007
Framingham (MI) 1991
UKPDS 68 MI 2004
DCS (MI) 2010
Framingham (CVD) 1991
SNDR (CVD) 2008
DCS (CVD) 2010
Fremantle (CVD) 2011
ADVANCE (CVD) 2011
UKPDS 56 (CHD) 2002
ARIC (CHD) 2003
Tayside (CHD) 2006
Hong Kong (CHD) 2008
0.3
et al. PDB54, ISPOR 18th Annual International Meeting, New Orleans, LA, USA, May 18-22, 2013
Dieren et al. Heart. 2012;98(5):360–9
IMS Health Confidential
Framingham (Stroke) 1991
UKPDS 60 (Stroke) 2002
UKPDS 68 Stroke 2004
Hong Kong (Stroke) 2007
Framingham (MI) 1991
UKPDS 68 MI 2004
DCS (MI) 2010
Framingham (CVD) 1991
SNDR (CVD) 2008
DCS (CVD) 2010
Fremantle (CVD) 2011
ADVANCE (CVD) 2011
UKPDS 56 (CHD) 2002
ARIC (CHD) 2003
Tayside (CHD) 2006
Hong Kong (CHD) 2008
ADDITION baseline CV r isk
Risk equation
A) To predict the baseline
CVD risk of the
ACCORD and
ADDITION population
ACCORD CV risk reduction
Risk equation
• REs were coded and
validated in Microsoft
Excel
Risk equation
• 10 contemporary risk
equations (REs) were
selected from a
systematic review2
ACCORD baseline CV risk
Framingham (Stroke) 1991
UKPDS 60 (Stroke) 2002
UKPDS 68 Stroke 2004
Hong Kong (Stroke) 2007
Framingham (MI) 1991
UKPDS 68 MI 2004
DCS (MI) 2010
Framingham (CVD) 1991
SNDR (CVD) 2008
DCS (CVD) 2010
Fremantle (CVD) 2011
ADVANCE (CVD) 2011
UKPDS 56 (CHD) 2002
ARIC (CHD) 2003
Tayside (CHD) 2006
Hong Kong (CHD) 2008
Framingham (Stroke) 1991
UKPDS 60 (Stroke) 2002
UKPDS 68 Stroke 2004
Hong Kong (Stroke) 2007
Framingham (MI) 1991
UKPDS 68 MI 2004
DCS (MI) 2010
Framingham (CVD) 1991
SNDR (CVD) 2008
DCS (CVD) 2010
Fremantle (CVD) 2011
ADVANCE (CVD) 2011
UKPDS 56 (CHD) 2002
ARIC (CHD) 2003
Tayside (CHD) 2006
Hong Kong (CHD) 2008
0.00
0.02
0.04
0.06
0.08
Conclusions and further proceedings
IMS integrated eight alternative CV-REs into the CDM
“While this research demonstrated that UKPDS REs perform “on average”
versus newer sets of equations….”
…IMS decided to integrate new CV risk equations in the CDM
1. To address concerns from HTA
– E.g. TLV now requests use of S-NDR based REs for model submissions in Sweden
2. To cover relevant geographical regions (US, Asia, Europe, Nordics, Australia)
3. To expand the scientific scope of the model
– Perform scenario analysis using different CV-REs
– Demonstrate implications of using different CV-REs
18
IMS Health Confidential
Eight New CV risk prediction models
Summary
End Point
19
Diabetes
Type
Data set
Region
Registry
Nordics
RCT
Randomized cohort
20 countries from
Asia, Australia,
Europe, and
North America
2
Observational
prospective
50K employees
Germany
(validated in Asia)
2
Observational
prospective
1
Swedish NDR
CVD=sudd card death, fatal IHD, nf MI,
unst. Ang, PCI, CABG or f/nf stroke
2
ADVANCE risk
engnie
CVD =
CHD or stroke
3
PROcam
CHD event
4
ARIC
CHD event
5
FREMENTLE
CVD event = MI or stroke
2
6
HONG KONG
RE 1 for stroke
RE 2 for HF
7
Pittsburg EDC
CHD defined as CHD death, fatal/nonfatal MI or Q-waves
8
EDIC
CVD=MI or stroke or IHD
IMS Health Confidential
Study type
2
2
Observational
retrospective
Community based
cohort
US
Observational
prospective
Community based
cohort
Australia
2
Observational
retrospective
Registry
Asia
1
Observational
prospective
Population
epidemiologically
representative of all
T1D in Pennsylvania
US
1
Interventional
turnedobservational
US
Eight New CV risk prediction models
Can be selected as alternative choices in the simulation input interface
CVD risk models available in CDM v8.0
• Framingham
• UKPDS risk engines (UKPDS 56/60)
• UKPDS OM1 (UKPDS 68)
Type 1 diabetes specific equations
20
IMS Health Confidential
Contrasting eight cardiovascular risk equations for use in
type 2 diabetes cohorts
Poster – EASD, Stockholm, Sweden, 14-18 September 2015
Objective
• Compare cardiovascular incidence
and predicted ICER (per QALE)
across risk equations (RE)
Methods
• Baseline characteristics from the
EDGE study
• Comparators: MET+DPP4
(Vildagliptin) vs. MET+ SU
• Treatment Effects
• UK setting
21
IMS Health Confidential
Contrasting eight cardiovascular risk equations for use in
type 2 diabetes cohorts
Conclusions
1. There was a noteworthy
difference in predicted CI of CV
events across the eight REs
2. Therefore economic assessments
should utilize REs from the most
representative population
3. Importantly, both the estimated
QALE and ICERs were relatively
stable across risk equations
4. Due principally by the health
economic benefit being driven by
quality of life improvements due to
hypoglycaemia risk reductions and
less weight gain in the M+D arm
22
IMS Health Confidential
Type 1 update
Microvascular disease (EDIC)
Macrovascular (new T1D CV REs)
Mortality (T1D mortality RE)
Alternative option to model micro-vascular complications
Microvascular disease
The EDIC observational follow up study of the DCCT
1,441 subjects
1,375 subjects (annual examinations)
Risk reduction with INT vs. CON
Metabolic memory
24
IMS Health Confidential
Microvascular disease
CDM Approach 1 (transition probability tables)
CDM clinical tables
Background transition probabilities derived from DCCT
cumulative incidence
p = p (BG_int/con) * RR(HbA1c) * RR(SBP) * RR(ACE) * RR(Laser) * RR(Ethnicity)
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IMS Health Confidential
Microvascular disease
Renal
Eye
Updated based on
DCCT/EDIC
Metabolic memory
adjustments added
Updated
Updated
Updated
Updated
CDM v9.0
Background
risk
INT/CON
A1C
ACE
SBP
BDR onset
BDR-PDR
PDR-SVL
DCCT/EDIC1
DCCT/EDIC1
DCCT/EDIC
INT only
INT only
X4
X4
X4
*/*
*/*
*/*
no change
no change
no change
No-ME
DCCT/EDIC1
INT only
X4
*/*
no change
X4
MAU onset
DCCT/EDIC2
INT only
X5
*/*
no change
X2
MAU-GRP
DCCT/EDIC2
INT only
X6
*/*
no change
X8
GRP-ESRD
DCCT/EDIC2
X
*/*
no change
X2
Neuropathy
DCCT/EDIC3
X7
X
no change
HbA1c adjustments
updated based on EDIC
26
Conventional treatment
removed
IMS Health Confidential
INT only
ACE adjustments
removed
1)
2)
3)
4)
5)
6)
7)
8)
New
Laser/no laser
therapy
Race
Metabolic
memory
X4
X4
X4
no change
no change
X8
Lachin et al. DCCT/EDIC research group.Diabetes. 2015 Feb;64(2):631-42
de Boer IH. DCCT/EDIC Research Group, Diabetes Care 2014;37:24–32
Albers JW et al. DCCT/EDIC Research Group, Diabetes Care. 2010 May;33(5):1090-6
White NH et al. DCCT/EDIC Research Group, Arch Ophthalmol. 2008 Dec;126(12):1707-15
DCCT/EDIC Research Group. JAMA. 2003 Oct 22;290(16):2159-67
de Boer et al. Arch Intern Med. DCCT/EDIC Research Group, 2011 Mar 14;171(5):412-20
Martin CL et al. DCCT/EDIC Research Group.Diabetes Care. 2014;37(1):31-8
Gubitosi-Klug RA et al.DCCT/EDIC Research Group.Diabetes Care. 2014;37(1):44-9
Type 1 update – Macrovascular disease
Two T1D specific CVD risk models were added to the CDM
Pittsburg CVD model
• Epidemiology of Diabetes Complications Study (EDC)
• Long term prospective observational cohort study
• Includes 658 subjects with childhood onset T1D diagnosed
before the age of 17
• Follow up ongoing since 1988 (22- years)
Risk factors
• T1D duration
• Background risk based on CVD incidence
during EDIC
• HbA1c adjustment based on EDIC
• Risk score is weighted based on UKPDS 68
risk profile
Risk factors
• T1D duration
• White blood cell count
• HbA1c
• Total Cholesterol
• SBP
• HDLc
• Total Cholesterol
• nonHDLc
• HDL
• Waist/hip ratio
• Smoking
• SBP
• Microalbuminuria or greater
• ACE
27
EDIC CVD model
IMS Health Confidential
Research Poster presented at this conference
PDB72: Contrasting predictions of cardiovascular incidence derived from alternative risk prediction
models in Type 1 diabetes
Conclusions
I. Differences between T1D models
may be associated with population
characteristics
a. EDC population was
epidemiologically representative of
T1D cases (less stringent glucose
control)
b. DCCT/EDIC were fairly well
controlled in early stages of the
disease
II. UKPDS model underestimated
CVD
28
IMS Health Confidential
Type 1 update – Mortality
A new mortality risk equation specific to T1D and T2D was added
WA mortality equations1
• Two UKPDS mortality equations (event fatality & long
term diabetes mortality) were refitted based on data
derived from administrative dataset from Western
Australia (WA)
The WA mortality equations can be
selected in the simulation input
interface:
• Hospital and mortality records on 13,884 patients
• Specific to T1D or T2D populations
Risk factors
• Gender
• Age
• Focus Event (stroke, MI, HF, ESRD, ulcer,
amputation)
• Event history
• Diabetes type
1
29
Hayes A et al. Journal of Diabetes and Its Complications 27 (2013) 351–356
IMS Health Confidential
Alternatively, for non-WA based
predictions, a T1D morality adjustment
can be applied from the Clinical setting:
Alternative option for modelling
microvascular complications
Alternative option for modelling microvascular complications
Based on parametric curve fitting
Cumulative incidence of BDR
1
Original data from DCCT
The DCCT Research Group.
The effect of intensive
treatment of diabetes on the
development and progression
of long-term complications in
insulin-dependent diabetes
mellitus. N Engl J Med
1993;329:977-86
Cumulative incidence of PDR
0.4
0.8
0.3
0.6
0.2
0.4
0.1
0.2
0
0
10
20
30
Study Year
Observed (INT)
Observed (CON)
0
0
10
20
30
Study Year
Observed (INT)
Observed (CON)
Cumulative incidence of SVL
0.025
Additional data from EDIC
Lachin JM, White NH, Hainsworth DP, Sun W,
Cleary PA, Nathan DM. Effect of intensive diabetes
therapy on the progression of diabetic retinopathy in
patients with type 1 diabetes: 18 years of follow-up in
the DCCT/EDIC. Diabetes. 2015 Feb;64(2):631-42.
doi: 10.2337/db14-0930. Epub 2014 Sep 9
0.02
0.015
0.01
0.005
0
0
10
20
30
Study Year
Observed (INT)
Observed (CON)
31
IMS Health Confidential
General approach
Fitting Weibull regression equations to DCCT/EDIC data
• Fitting
progression from
BDR to PDR
• Data from DCCT
and EDIC
combined
• Cumulative
incidence related
to published data
Cumulative incidence of PDR
Fitted Weibull Hazard:
Where, t = duration of BDR
0
Coefficient
λ0
k
β HbA1c
β Hyperlipidemia
β MAU
β MAP
β CON
32
IMS Health Confidential
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
Value
158.88
1.21
0.39
0.33
0.93
0.04
0.65
Offset
5
10
15
20
Study Year
Fitted (Int)
Observed (INT)
25
Fitted (CON)
Observed (CON)
7.07
85
30
(BDR: background diabetic
retinopathy, CON: conventional arm in
DCCT, MAP: mean arterial pressure,
MAU: microalbuminuria, PDR:
proliferative diabetic retinopathy)
Illustrating changes in HbA1c and predicted incidence
DCCT/EDIC HbA1c progression compared to maintained DCCT profile
Cumulative Hazard
HbA1c Profiles (Observed)
HbA1c (%)
10
9
8
7
6
0
5
10
15
Study Year
INT
20
25
0.2
0.1
0
0
Cumulative Hazard
HbA1c (%)
9
8
7
6
10
0.3
CON
10
5
0.4
30
HbA1c Profiles (Example)
0
BDR to PDR
0.5
15
Study Year
20
25
30
5
10
33
IMS Health Confidential
CON
20
25
Fitted (Int)
Fitted (CON)
Observed (INT)
Observed (CON)
30
BDR to PDR
0.8
0.6
0.4
0.2
0
0
5
10
Fitted (Int)
INT
15
Study Year
15
20
Study Year
Fitted (CON)
Observed (INT)
25
Observed (CON)
30
Alternative option for modelling microvascular complications
Weibull parametric curves fitted to DCCT/EDIC Data
Retinopathy
Nephropathy
Neuropathy
From
None
BDR
PDF
None
ME
None
MA
MA
None
To
BDR
PDF
SVL
ME
SVL
MA
GPR
ESRD
DPN
Diabetes
Duration
Years
since
BDR
N/A
Diabetes
Duration
N/A
Diabetes
Duration
Years
since
Years
since
MA
MA
t variable
Diabetes
Duration
λ
9.71
158.88
10.87
95.75
19.61
53.39
31.67
43.33
69.24
k
0.42
1.21
1.00
1.29
1.00
1.64
0.80
2.18
1.34
HbA1c (centred on 7.07)
0.39
0.62
0.51
0.22
0.56
Smoker
0.29
RAASi
-0.04
SBP (centred on 120)
0.05
0.01
DBP (centred on 80)
0.07
Hyperlipidaemia
0.33
0.33
μAU
0.58
0.93
MAP (centred on 85)
0.02
0.04
Laser therapy
Exposure to CON
34
Macular Edema
IMS Health Confidential
0.89
-1.63
0.65
-0.41
0.36
-0.36
0.36
0.17
Retinopathy data and equation fit
BDR to PDR
Incident ME
• Limited reporting of any new data
and very low event numbers in
EDIC do not allow for robust
estimation of incidence of SVL
from PDR or ME
Incident BDR
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PDR to severe vision loss
ME to severe vision loss
Nephropathy and neuropathy data and fit
Incident MA
GPR to ESRD
• Risk equations fitted to
neuropathy data published from
DCCT and EDIC
MA to GPR with
impaired GFR
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MA to GPR
Neuropathy
NICE feedback on CDM-T1D
NICE critiques on T1D CDM and 9.0 answer
Item/area of
concern
Clinical data
available
Short-term
outcomes
associated to
hypoglycemia
events
NICE critique
Favors interventions
targeted to reduce
HbA1c and frequency
of long-term
complications
CDM v9.0 (updates)
• CDM also accounts for short-term effects on
− Hypoglycemia and ketoacidosis
− Weight changes reflect in quality of life
− Drug adverse events are also available for T2D
• Six different hypoglycemia types can be classified by
severity level and period of the day
Difficult to capture
− Non severe
− Severe grade 1-requiring 3rd party assistance
− Severe grade 2-requiring medical assistance
• The above can be diurnal or nocturnal
• Available using
Time changing
hypoglycemia
rates
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− Treatment algorithms in TD1 or,
− Newly integrated multivariate regression approaches
Not available
*As optional, published numerical approaches are
available to evaluate the impact of diurnal and nocturnal
of non-severe hypoglycemia events as the frequency
increases
NICE critiques on T1D CDM and 9.0 answer (contd.)
Item/area of
concern
Risk equations
to predict longterm
complications in
T1DM
May be dated and
underestimating the
risk of long-term
complications
Observational
data from
registries
To assess the
incorporation of recent
data (ex. Swedish
National Diabetes
Register for T1D)
Healthcare
resource use
related to
hypoglycemia
events
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NICE critique
IMS Health Confidential
Further data collection
needed
CDM v9.0 (updates)
• Eight additional sets of risk equations to predict
cardiovascular risk
− 2 for T1D
− Including Microvascular disease risk equations based on
EDIC study
• Represent different and important geographic regions
• New risk equations are based on recent observational
data
− Swedish National Diabetes Register
− Hong Kong Diabetes Register
• An excel module is available to calculate costs of
hypoglycemia on the basis of healthcare resources
required to treat hypoglycemia. The tool includes:
−
−
−
−
−
Emergency room
Hospital visits, outpatient treatments
Physician visits
Phone calls
Use of glucagon injections
Other news
CDM UI v9.5
Migration from 32 to 64 bit architecture
1. Primary focus is improved user experience
2. Technologies available today opens performance improvement opportunities
3. Currently available system runs on outdated platform
4. Migration to new architecture gives us new opportunities to extend the
functionality of CDM and continuously improve user experience
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CDM UI v9.5
Item list changes
New CDM Group location
Simplified action selection
Note: for completed simulations
download to Excel icon available
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CDM UI v9.5
New item page changes
New upload panel
Tooltip
based
on user
guide
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CDM Group removed
CDM UI v9.5
Simplified Clinical table management
Upload functionality
available
Multiple item
selection
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The Reference Manual
• The CDM is a complex model with many data inputs and many drivers
• A document is needed to explain all what is in the CDM
• The reference manual is this document
• Outdated versions are existing
• By end of January 2016 we expect it to be ready
• Important tool for HTA bodies
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Future CDM access and password change
User hygiene is of extreme importance both for IMS and for users
• CDM password is linked to a company e-mail address (not private)
• Password is temporary: Expires every three months
• How to receive a password (first time): Send e-mail to
IMSeService@imshealth.com and check whether access can be granted will
be done
• How to change password?
– User will receive an e-mail with a request to change password 7 days
before expiring
– This e-mail will include a link
– Password can only be changed by following this link (within 14 days after request
to change)
– Not changed in time, no longer access, so request to be renewed
– No direct possibility to change password via the web
• Purpose: People that leave company will not be able to access CDM anymore
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License Terms
• Company license:
– Renewal required annually
– Thus expires for all it’s employees automatically
– IMS can extend period for 1 month to support renewal contract
• Academic license:
– Expiration date at user level
• HTA reviewer license
– Duration agreed between company and IMS
– Access is not complete: Only review of settings
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Practical guidance on the use
of the model
The choice for a web based patient
simulation model approach using C++
Model Taxonomy
Different methodological appraoches
Decision tree: Estimates likelihood of
various outcomes and associated pay
offs without modeling the time of
outcomes
...majority of
approaches
are STM:
State transition model: Employ a
descrete time approach (model cycles).
Are also referred to as Markov Models
Descrete event simulation model:
Progress through the times at which
specific events happen. DESs are
inherently patient-level
Dynamic transition model: Disease
progression of indiiduals depends on
others being diseased (infectious
diseases)
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...beyond, model approaches
differ in the way they move
the population through the
model strucutre:
Cohort approach
Patient level
appraoch
DES requires PLS
DTM requires PLS
Model Taxonomy
Cohort model vs. Patient level simulation approach
Model structures differ in the way they process the diseased population through the
time path
A. The patients are moved individually through the model structure
B. The entire cohort is moved as a whole through the model structure
Economic
models can
bei either
Patient level
simulation (PLS)
approach
Outcomes modeled
for individual patients
and averaged
Cohort model
(CM) approach
Outcomes modeled
for the cohort as a
whole
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Example: Transition probability of 0.05 to
move from A to B in year 1
Implications
Monte Carlo:
if RAND(0-1)< 0.05 then AB
i. Requires random
generator at the decision
node
ii. Stochastic uncertainty
iii. high run time
i. Simpler
ii. Can be solved in a
spreadsheet approach
iii. No stochastic
uncertianty
iv. Complexity limitations
Cohort Models
Complexity limitations
Cohort models are memoryless
(marcovian propery)
A. Parallel complications have to
be represented by „replicating
states“
B. Time in state has to be tracked
using complex „Tunnel
States“ approaches which are
computationally expensive
C. Provide biased estimates if
variable patient properties
which have non-linear
relationsip with outcomes are
evaluated at the mean
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NICE position1:
I. It should be noted that, depending on the
complexity of the clinical scenario being
modelled, the number of logical rules required to
make the transition matrices dependent on the
patient’s previous history, or the occupation time
within each state, may make programming
errors difficult to detect if the model is
implemented within a spreadsheet package”
II. If there are factors which vary between patients
(e.g. age) which have a non-linear
relationship with the model outcomes (e.g.
costs and QALYs), then estimating the model
outcomes for a cohort of patients using only
average characteristics (e.g. mean age at
starting treatment) will provide a biased
estimate of the average outcome across the
population to be treated“1
Complexity limitations in Spreadsheet approaches
• State-Transition Models (STM) consist of a discrete set of mutually exclusive Health
States (HS)
3 complications
• In order to track the occurrence of parallel complications, the set of mutually exclusive
health states has to be extended
1
3
3
0 compliction
1 compliction
2 parallel complictions
No comp.
A
A&B
B
B&C
1
= 8 HS
3 complications
A&C
A&B&C
4 complications
C
4
6
1
One complication only
States required to consider history of 2
complications
No comp.
A
A&B
A&C
B
B&C
B& D
C
C&D
D
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4
A& D
1
States required to consider
history of 3 complications
A&B&C
A&B&D
A&C&D
B&C&D
History of 4
complications
A&B&D&C
= 16 HS
The number of required health states in a model
implementation
… is based on the number of complications considered
Complications
0
1
2
3
4
4
1
4
6
4
1
5
1
5
10
10
5
1
6
1
6
15
20
15
6
1
7
1
7
21
35
35
21
7
1
8
1
8
28
56
70
56
28
8
5
6
7
8
1
Total
16
= 24
32
= 25
64
= 26
128
= 27
256
= 28
Pascals Triangle
N=
....
= 2n
The binominal coefficient is used in combinatorics to evaluate the total number N of possibilities
(combinations) to choose k elements from a set of n elements
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The number of required health states in a model
implementation
… as a function of the number of considered complications
Taking the example of the CDM -16 interdependent sub-models:
Angina, Myocardial Infarction (MI), Congestive Heart Failure (CHF), stroke, Peripheral Vascular
Disease (PVD), diabetic retinopathy, Macula Edema (ME), cataract, hypoglycaemia, ketoacidosis,
nephropathy, End-Stage Renal Disease (ESRD), neuropathy, foot ulcer, and amputation
Conclusions
Maximum number of health states required to
respesent complication history
A. CDM requires PLS
Health states required
300000
262143
250000
C. Possible in Excel?
200000
150000
131071
• Yes but requires VBA to code
PLS model structure
• Use Excel as front end
100000
CDM
50000
65535
32767
16383
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18
Number of distinct complications
1) McEwan et al. Pharmacoeconomics. 2010;28(8):665-74
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B. CM approach unfeasible
IMS Health Confidential
C++ provides a 90fold reduction vs.
VBA1
The decision for a Web based model
Back ends and front ends can be combined in arbitrary ways
Back End (calculation engine)
56
Front End (inputs and outputs)
Excel
Excel
Stand alone
Visual Basic
VB-Interface
Stand alone
Treeage
Treeage
Stand alone
C++
Web
Server based
IMS Health Confidential
The decision for a Web based model
Back ends and front ends can be combined in arbitrary ways
• Models are distributed across a range of involved stakeholders
• This can easily result in the requirement of >100 copies of the model
• Model updates become very complex/unfeasible if not managed on a
central server
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Conclusions
1. We apply a PLS approach in the model because CM is unfeasible
2. We use C++ since any alternative approach would lead to unacceptable run
time requirements
3. We use a Web based approach to optimize distribution and version control of
the model
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Random Walk
Random Walks
Origin
• In 1905 Karl Pearson described a simple model in which, at each time step, a
single mosquito moves a fixed length a, at a randomly chosen angle
B. Hughes, Random Walks and Random Environments, Vol. I, Sec. 2.1 (Oxford, 1995)
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How is this related to health economics?
Monte Carlo Simulation
Baseline
Year 1
Year 2
Year 3
Year 4
Year 5
Year 6
Year 7
Year 8
Year 9
Year 10
Year 11
Year 12
Year 13
Year 14
Year 15
Alive
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
P(Death)
Rand()
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.797
0.246
0.105
0.279
0.956
0.686
0.932
0.077
=if (random number)
< probability, death occurs
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Alive
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
P(Death)
Rand()
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.959
0.918
0.969
0.944
0.716
0.996
0.717
0.893
0.992
0.819
0.114
0.729
0.180
0.736
0.094
=random number (0-1)
Illustrating the inefficiency of Monte Carlo Simulation
Estimating the area of a triangle: ½*b*h
Relative Error
True Area − Estimate Area
=
True Area
Converges fairly rapidly to a good estimate
of the area
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Illustrating the inefficiency of Monte Carlo Simulation
Estimating the area of a smaller shape
Convergence is slower when we try to
measure a smaller effect
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Illustrating the inefficiency of Monte Carlo Simulation
Monte Carlo Estimate of the Area of an Even Smaller Shape
Convergence is very poor when
the effect we try to measure is
very small
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Monte Carlo Simulation in a Disease Model
Monte Carlo Estimate of a Survival Curve
Simple Algorithm:
• Every year attempt to
remove each subject with
probability 1/10
• Continue until no subjects
remain
• Plot the proportion of
subjects remaining each year
• Repeat
Observe that the simulations stabilize and converge when
the number of subjects is increased
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How many patients should you simulate?
Trade–off between accuracy and run-time
• The answer = it depends
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Number of bootstrap replications
Standard error of predicted QALYs
• The table below shows the relationship between the precision of
predicted QALE and cohort size and number of replications
Number of boostrap
replications
• ± 2× Standard Errors will give approximate confidence interval of mean
estimate
67
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
IMS Health Confidential
100
200
300
400
0.0135
0.0092
0.0076
0.0066
0.0058
0.0053
0.0050
0.0046
0.0044
0.0041
0.0092
0.0066
0.0054
0.0047
0.0042
0.0038
0.0035
0.0033
0.0031
0.0030
0.0074
0.0054
0.0044
0.0037
0.0034
0.0031
0.0029
0.0027
0.0025
0.0024
0.0065
0.0048
0.0038
0.0033
0.0029
0.0027
0.0024
0.0023
0.0022
0.0021
Cohort size
500
600
0.0061
0.0042
0.0034
0.0029
0.0026
0.0024
0.0022
0.0021
0.0020
0.0019
0.0053
0.0038
0.0031
0.0027
0.0024
0.0022
0.0020
0.0019
0.0018
0.0017
700
800
900
1000
0.0049
0.0035
0.0029
0.0025
0.0022
0.0020
0.0019
0.0018
0.0016
0.0016
0.0047
0.0032
0.0027
0.0023
0.0021
0.0019
0.0018
0.0016
0.0016
0.0015
0.0045
0.0031
0.0025
0.0022
0.0020
0.0018
0.0017
0.0016
0.0014
0.0014
0.0041
0.0029
0.0024
0.0020
0.0019
0.0017
0.0016
0.0015
0.0014
0.0013
Conclusion
• It is worth estimating the expected health benefit from a particular
scenario
– There is a tool now to help with this
• The precision of CDM output is a function of
– Variability of simulated scenario
– Cohort size
– Number of bootstrap replications
• We have look-up tables now to help inform on this choice
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Impact of BMI on study results
BMI as a driver of analysis outcomes
BMI impacts analysis outcomes via two routes: Direct and indirect
1. Directly impacts on patient quality of life
2. Indirectly impacts on patient quality of life and complication costs by affecting
risk of complications
Patient
quality of life
BMI
Risk of
complications
Complication
costs
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Indirect impact of BMI change on outcomes:
Complication rates
The impact of BMI on complication rates is smaller than you might think
UKPDS68
UKPDS82
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Indirect impact of BMI change on outcomes:
Complication rates (contd.)
The impact of BMI on complication rates is smaller than you might think
Swedish NDR
• Similar for HKDR (Hong Kong) and PROCAM risk equations
• BMI has no impact on complication rates when using Advance, ARIC or Fremantle
risk equations
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Direct impact of BMI change on quality of life
The relationship between weight/BMI and health utility has been widely reported
Decrease in health utility per unit increase in BMI
Change in Health Utility
0.025
0.020
0.015
0.010
0.005
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Solli et al.
Marrett et al.
Lee et al. (T2DM)
Lee et al. (T1DM)
Kontodimopoulos et al.
Hakim et al.
Dixon et al.
Currie et al.
Coffey et al. (T2DM)
Coffey et al. (T1DM)
Bagust et al.
0.000
Why Bagust?
The Bagust disutility is commonly applied, especially in HTA submissions
• The Bagust disutility (-0.0061) is NICE approved
– NICE has rejected other utility values, including those derived from patient level trial
data, and requested analyses be run with Bagust in past
• The Bagust utility comes from a high quality, comprehensive study
– Based upon a European type 2 diabetic population
• Note that multiple different values can be obtained from the study
– Depends on model chosen and the approach you are taking to disutility in your
analysis
• allowing for negative utilities: -0.0061
• or bounded between 0-1: -0.0038
• A meta analysed value, encompassing a greater body of evidence, may be a
more appropriate choice
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What’s the impact?
The direct impact of BMI on quality of life is much larger than the indirect impact
• If we look at the impact of a 1 kg/m2 decrease in BMI for 1 year in
1,000 patients
– The impact on complications (using the UKPDS68 risk equation) will result in 0.2
congestive heart failure events avoided. Using the Beaudet disutility of -0.108 this is
a difference in utility of about 0.02 QALYs
Change in utility
– The direct impact of BMI on quality of life is more significant. Using the Bagust
disutility (-0.0061) the direct impact on utility is approximately 6.1 QALYs, about
300 times the impact of the complication reduction
7
6
5
4
3
2
1
0
Direct
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Indirect
Recommendations
A considered approach should be taken to BMI and BMI disutility
• HTA submissions may require following the prescribed standard, however
other studies have more freedom to explore assumptions
• Be aware that chosen value may depend on other utility sources
• Any assumptions made should be tested in sensitivity analysis
– However, be aware that running sensitivity analyses around small differences in BMI
treatment effect may produce counterintuitive results as a result of variation due to
random walk (note that as for all parameters differences in BMI should be
statistically different)
– To illustrate a 0.1 kg/m2 treatment effect difference requires a minimum cohort size
of around 6,000 to demonstrate a treatment effect beyond random walk
• The key is ensure that you are using defendable values and assumptions
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Sense and nonsense of sensitivity
analyses
The rational beyond sensitivity analyses
• There is widespread agreement that the appropriate methods for
handling uncertainty can be collectively referred to as sensitivity
analyses
• Sensitivity analyses represent one of the key HTA requirements
• Uncertainty is considered explicitly in the process of arriving at a
decision by the HTA bodies
• Correlation between high levels of uncertainty and negative decision
have been indicated
• In seeking to address parameter uncertainty, both deterministic and
probabilistic sensitivity analyses should be undertaken
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Deterministic sensitivity analyses
• Deterministic analyses serve to highlight which model parameters are
critical to driving outcomes
• Practice in relation to univariate sensitivity analysis is highly variable,
with considerable lack of clarity in:
• Relation to the methods used
• The basis of the parameter ranges employed
• A consistent and justifiable rational regarding the parameter ranges
should be applied consistently to all the parameters included in the
sensitivity analyses
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Some sensitivity analyses should always be considered
• Several considerations should be kept in mind when deciding which
parameters need to be tested in your analyses:
• What are you trying to achieve with your sensitivity analyses?
• Which are the key economic parameters?
• Which are the main clinical drivers of your analyses?
• Are you submitting to a specific HTA body?
• Is there a significant difference between the two arms on a certain parameter?
– Base case: if not significant difference, to be put equal
– Test all non significant differences using the point estimates in 1 analysis
– Don’t run sensitivity analyses in case there is no significant difference (will only cause
confusion)
• Combination of extreme values?
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Example of sensitivity analyses that could be run in the CDM
Most common sensitivity analyses
Change from baseline HbA1c in intervention arm equal to comparator / Upper and Lower
values
Change from baseline SBP in intervention arm equal to comparator / Upper and Lower
values
Change from baseline BMI in intervention arm equal to comparator / Upper and Lower
values
No disutility per unit BMI
Change in discount rates on costs and benefits
Shorter time horizon (eg. 5 years)
Different risk equations
Different HbA1c threshold for treatment discontinuation
Different time spent on treatment
Variation in the complication costs
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Q&A
IMS82
Health Confidential
Thank You