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Karnon et al, Modeling assumptions impact analysis

1

A critique and impact analysis of decision modeling assumptions

(running head: Modeling assumptions impact analysis)

Jonathan Karnon, PhD, School of Health and Related Research, University of

Sheffield

Alan Brennan, MSc, School of Health and Related Research, University of Sheffield

Ron Akehurst, Hon MFPHM, School of Health and Related Research, University of

Sheffield

Conflict of Interests: The School of Health and Related Research at The University of

Sheffield, in which the authors are based, developed a model of the cost-

effectiveness of clopidogrel to be included in a submission to the National Institute for

Clinical Excellence on behalf of Bristol-Myers Squibb and Sanofi-Synthelabo. The

work described in this manuscript has not been funded by these companies, none of

the authors is in personal receipt of payment from these companies, and the

companies have had no editorial or drafting input into this manuscript.

Correspondence: Jonathan Karnon, School of Health & Related Research, University

of Sheffield, Regent Street, Sheffield, S1 4DA

Email: [email protected]


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A critique and impact analysis of decision modeling assumptions

Abstract

Background: Numerous guidelines have been published defining good practice for

the conduct of economic evaluations in general, and model-based evaluations in

particular. The extent to which guidelines are accepted is unknown, and the impact of

deviations from good practice is not generally recorded. We identified four specific

issues in applied studies that may impact on the accuracy and comparability of

different evaluations.

Methods: A descriptive analysis of four modeling issues (inclusion of incident cases

over a model time horizon, appropriate time horizon, parsimonious model structure,

and the handling of age-specific sub-groups) is presented. A case study model is

analysed to illustrate the quantitative impact of three of the issues.

Results: In the case study model, alternative specifications of the modeling

framework are shown to alter the estimated cost-effectiveness by large percentages.

The combined effect of including incident cases and reduced follow-up yielded the

highest divergence from the reference case results, by between 20% and 40%

depending on the age group. Reference case results of an age-weighted population

were almost 14% different from the middle single age cohort.

Discussion: The identified issues are all generalisable to a wide range of treatment

areas and are, or should be, addressed by evaluative guidelines. The authors call for

the continued development, dissemination, and application of guidelines for the

conduct of economic evaluation in general, and model-based economic evaluations

in particular.

Keywords: Guidelines; Models, Decision Support; Cost-effectiveness; Antiplatelet

Drugs


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A critique and impact analysis of decision modeling assumptions 1

Introduction 2

The estimation of the full economic effects of health technologies generally requires 3

the extrapolation of clinical trial evidence beyond the period of follow-up through the 4

use of decision modeling techniques to synthesise data from disparate sources. 5

Numerous guidelines have been published detailing appropriate conduct for the 6

application of modeling evaluations, which have been the subject of a methodological 7

systematic review.[1] The review recognises that methodological aspects of the 8

modeling process may be explicitly defined in the form of guidelines, though such 9

checklists cannot be expected to inform the appropriateness of issues such as the 10

specification of the model structure. 11

Through general use and review of the health economics literature we have identified 12

a number of specific issues relating to the application of decision modelling as a 13

framework for the estimation of the long-term costs and effects of alternative health 14

care interventions. The primary issue concerns the inappropriate inclusion of incident 15

cases over the time horizon of an evaluation, which was identified in an application of 16

the Coronary Heart Disease Policy (CHDP) model,[2] to analyse the cost-17

effectiveness of alternative antiplatelet therapies.[3] Secondary issues emanating 18

from our investigation include the use of an appropriate time horizon, a parsimonious 19

model structure, and the handling of (age-specific) sub-groups. This paper describes 20

the theoretical aspects of each of the above issues and quantifies the impact of 21

applying alternative approaches to three of the issues (an impact analysis of the 22

parsimony issue would require a multi-modelling centre approach that was 23

considered infeasible for this study). The aim is to highlight the need for the common 24

application of guidelines that address these specific modeling issues. 25


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Identifying the issues 26

The following sections describe the basis for our assessment of the four identified 27

issues: inclusion of incident cases over the time horizon of an evaluation, appropriate 28

time horizon, a parsimonious model structure, and the handling of age-specific sub-29

groups 30

Incident cases 31

Gaspoz et al used the CHDP model to estimate the incremental cost-effectiveness of 32

aspirin, clopidogrel, or combined aspirin plus clopidogrel for the secondary 33

prevention of coronary heart disease.[3] It is stated that the evaluation ‘modelled U.S. 34

patients, 35 to 84 years of age, in whom coronary disease developed during or 35

before 2003 to 2027 and who survived their first month with it’. 36

The evaluation configured the CHDP model to predict the costs and QALYs 37

associated with CHD in the US population over a 25 year period. This means that 38

incident patients are added to the model every year over the 25 year period, but that 39

the model follows all patients only to the end of the 25 year period, i.e. patients 40

diagnosed in year 20 are followed up for 5 years, irrespective of their age at the end 41

of the follow-up period. This approach provides an incomplete analysis of the full 42

impact of the interventions being evaluated, for which the downstream costs and 43

benefits of avoiding secondary cardiovascular events in the short-term are excluded. 44

If incident cases are to be included in a model-based evaluation, then the time 45

horizon of the evaluation should be extended such that the costs and effects are 46

measured over the same timeframe (i.e. to the same upper age limit) for every 47

individual that enters the model. If this rule is implemented, in most treatment 48

evaluations the modelling of incident cases will affect only the absolute cost and 49

effect estimates as the relative cost-effectiveness of alternative interventions will 50

remain the same. Exceptions will occur in cases in which the characteristics of the 51


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patient population vary over time, such as changes in the age distribution or disease 52

severity. Though, if identifiable characteristics are shown to significantly alter cost-53

effectiveness results then sub-group analyses may be relevant. 54

Time horizon 55

A commonly identified area of variation between modelling studies is the defined time 56

horizon of models, which describes the maximum age to which individuals are 57

followed. In the applied CHDP model,[3] the choice of a 25 year time horizon for the 58

evaluation of a population aged between 35 and 84 years may also be questioned. 59

The youngest individuals entering the model are followed up to the age of only 60 60

years, and even individuals aged 50 years (an age at which CHD incidence is 61

considerable) may gain significant QALYs after the age of 75 years. 62

There are numerous examples of the specification of non-lifetime horizons in models 63

evaluating interventions in a wide range of disease areas.[4,5] The impact on the 64

accuracy of the cost-effectiveness results will vary, though it will increase in line with 65

decreasing mortality rates, time horizons, and discount rates. 66

Parsimony 67

Weinstein et al advise that “[t]he structure of the model should be as simple as 68

possible, while capturing underlying essentials of the disease process and 69

interventions”,[pg12] in their report on Principles of Good Practice for Decision 70

Analytic Modeling.[15] 71

The CHDP model aimed to estimate the health effects and costs of coronary heart 72

disease in the US population.[2] This is a complex state transition model comprising 73

three linked models describing the incidence of coronary disease in the unaffected 74

population, the first 30 days of an initial coronary event, and the disease history of 75

patients who survive the first 30 days following a coronary event. The final section of 76

the CHDP model (the Disease History model) includes four health states in months 77


6

2-12 following diagnosis (angina, acute myocardial infarction (AMI), cardiac arrest, or 78

AMI plus cardiac arrest). Eight states describe the subsequent natural history, 79

representing patients’ coronary disease history as alternative combinations of the 80

experience of three events: cardiac arrests, AMIs, and revascularisation in the form 81

of a coronary artery bypass graft (CABG). Patients not experiencing any of these 82

events have, by default, angina. From these eight states patients are assigned 83

annual transition probabilities of experiencing an AMI, cardiac arrest, CABG, or non-84

coronary death. 85

The evaluation of antiplatelet therapies using the CHDP model illustrates the link 86

between unnecessary model complexity and potential modelling errors. Firstly, as a 87

result of identified errors the authors’ were required to revise the originally published 88

results.[6] In particular, one error occurred in the estimation of the benefits in 89

reducing the risk of stroke, an area that was not covered by the original CHDP model 90

and was incorporated into the model in the form of a derived reduction in the overall 91

rate of death from non-coronary causes. 92

Secondly, a lifetime duration of clopidogrel therapy is assumed in the original Gaspoz 93

analysis, though in response to de Lemos and McGuire,[7] Gaspoz et al specify a 94

therapy option of clopidogrel plus aspirin for all patients in the 1st year post-CHD 95

diagnosis. The results of the short-term treatment option are counter-intuitive, 96

showing that Clopidogrel is less cost-effective when treatment is confined to the first 97

year post-diagnosis – the time period over which patients are at increased risk of 98

further vascular events. Gaspoz et al do not discuss the foundations of this counter-99

intuitive result, and one cannot discount the possibility that a more parsimonious 100

model, describing only the relevant events and providing more flexibility in the 101

representation of the transition probabilities, would have produced alternative results. 102


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Sub-group analysis 103

Dewilde & Anderson present a very interesting paper that shows the importance of 104

incorporating the age distribution of the eligible population when modelling the cost-105

effectiveness of screening, rather than simply modelling the pathways of patients 106

from the age at which they first become eligible for screening.[8] Using cervical 107

cancer screening as a case study, they show that the standard approach of 108

modelling a single screening cohort (from age 15 years) and an age-weighted 109

approach in which costs and effects are estimated for 11 separate age-based cohorts 110

result in mean incremental cost-effectiveness ratios of Aus$47,361 and Aus$61,031, 111

respectively (discounted at 5%). 112

The single cohort approach estimates the cost-effectiveness of screening once the 113

screening programme reaches equilibrium, i.e. all eligible women were first offered 114

screening at age 15 years. In the case of cervical cancer screening, equilibrium 115

would not be reached for 45 years based on five year intervals between age cohorts 116

and a final screening age of 65 years. An alternative approach would be to treat the 117

different age cohorts as sub-groups of the eligible population, with the possibility of 118

defining age-based screening policies. This latter approach has been adopted in 119

various model-based screening evaluations.[9,10] 120

The findings of Dewilde & Anderson[8] are likely to be applicable to treatment 121

evaluations, particularly in areas such as CHD where event rates have been shown 122

to vary significantly with respect to age.[11] Where age-based sub-groups are 123

feasible from a policy implementation perspective, separate analyses by age should 124

be undertaken. If it is not possible to implement age-specific policy 125

recommendations, the age distribution of the eligible population should be 126

represented, and the approach presented by Dewilde & Anderson provides a suitable 127

method for undertaking such analyses. 128


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Modeling the impact of alternative approaches 129

A comparison of the use of a parsimonious versus a non-parsimonious model 130

structure is difficult to undertake and require the application and independent 131

analysis of multiple models as the hypothesised advantages of parsimony relate to a 132

reduced probability of error. However, the three other issues raised above may be 133

investigated further by the application of alternative methods of analysis to a single 134

case study model, which is used to demonstrate the impact of including incident 135

cases over the course of the specified time horizon, and the use of a short time 136

horizon relative to expected survival. The case study model is also analysed to 137

estimate the impact of modelling alternative age-based patient sub-groups: 138

- a single age cohort based on the mean age of the patients informing the input data, 139

- multiple age cohorts with the results presented separately for each cohort, 140

- multiple age cohorts with the results aggregated based on the age distribution of the 141

eligible population. 142

Methods 143

The case study model is one of our own previously published models incorporating a 144

wide range of age-specific data. The chosen model evaluates the cost-effectiveness 145

of one year of clopidogrel in addition to aspirin compared to aspirin alone in patients 146

with newly diagnosed acute coronary syndromes (ACS).[11] 147

A decision tree covering the one-year treatment period describes patient pathways 148

with respect to alternative forms of revascularisation over the one-year treatment 149

period (Figure 1). Major bleeding event rates are linked to the incidence of coronary 150

artery bypass graft (CABG), and percutaneous coronary intervention (PCI) rates. The 151

summed proportions of patients in each of the end states of the decision tree (event 152

free, non-fatal MI, non-fatal stroke or vascular death) inform the distribution of 153

patients across the start states in a lifetime Markov model. The Markov model 154


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describes the annual probability of patients experiencing a MI, stroke, or death from 155

vascular or non-vascular causes (Figure 2). Following the experience of either a MI 156

or stroke, patients may either remain in that state or progress to the death state. 157

Alternative states are used to describe mortality rates for patients in the first year of a 158

MI or stroke event, and for patients who have survived their first year following a MI 159

or stroke. 160

Data sources used to populate the model are described in more detail elsewhere.[11] 161

In summary, revascularisation and bleeding event rates were based on aggregate 162

(non-age-specific) analysis of prospective and retrospective studies of ACS patients 163

in the UK.[12,13] Patient-level data from the Prospective Registry of Acute Ischaemic 164

Syndromes in the UK[12] and the Nottingham Heart Attack Register (personal 165

communication D Gray) were available for analysis to inform age-specific event rates 166

for ACS patients from diagnosis, as well as following the experience of an MI. The 167

estimated event rates accounted for higher event rates in the period immediately 168

following an event by undertaking logit regression analysis to estimate initial event 169

rates, and exponential survival analysis to estimate constant longer term event rates 170

(time varying distributions were tested and were not found to be statistically 171

significantly different). General population mortality rates, minus age-specific 172

proportion of deaths due to vascular causes, were also applied in the model. 173

No data were identified describing subsequent event rates in ACS patients 174

experiencing stroke. Data are available describing event rates in all stroke patients 175

(i.e. not limited to stroke patients previously diagnosed with ACS),[13] but these rates 176

are unrealistically low in comparison to the observed event rates in patients 177

remaining event-free following ACS. A conservative assumption is made that ACS 178

patients experiencing stroke during the one-year treatment period have the same 179

subsequent event rates as ACS patients remaining event-free. 180


10

Treatment effectiveness was represented as the relative risk (RR) of four clinical 181

outcome events - vascular death, non-fatal MI, non-fatal stroke, and major bleeding. 182

The RRs were obtained from the CURE trial, which randomised 12,562 patients 183

within 24 hours of the onset of non-ST-segment-elevation ACS to standard therapy 184

including aspirin (75-325 mg), or to clopidogrel (300 mg loading dose and then 75 mg 185

daily) on top of standard therapy including aspirin.[14] Cost and utility data were 186

obtained from published sources and are described in Table 1, as are the RRs. 187

Model analyses 188

The reference case analysis of the model is defined to exclude incident cases, 189

following a patient cohort of 60 year olds newly diagnosed with ACS to a maximum 190

age of 100 years. Costs and QALYs are both discounted at an annual rate of 3.5% 191

(currently recommended in the UK). Subsequent analyses cover all combinations of 192

including and excluding incident cases, modelling patient cohorts of 50, 60, and 70 193

year old patients, and maintaining the maximum age 100 years timeframe or 194

reducing the timeframe to a 20 year follow-up period. The results of these analyses 195

are also combined to estimate age-weighted aggregate estimates of costs and 196

QALYs. 197

Results 198

The results of the analyses based on alternative sets of framework assumptions are 199

presented in Table 2 for a single age patient cohort. The reference case analysis 200

(number 1: 60 year old patients, excluding incident cases, followed to a maximum 201

age of 100 years) shows average costs per patients of almost £16,000 (difference 202

between treatment groups of £393), QALY gains of around 9 QALYs (difference 203

between treatment groups of 0.036), leading to an incremental cost-effectiveness 204

ratio (ICER) of £11,054. 205


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Compared to the reference case, analysis number 2 (in which a shorter time horizon 206

of 20 years is specified) shows a very small increase in the ICER (0.1%), which is 207

due to the high mortality rates associated with ACS – after 20 years over 85% of a 60 208

year old cohort receiving ASA+clopidogrel are expected to have died. In analysis 209

number 3, the lone effect of modelling incident cases over a 40 year time horizon is 210

not large (7.3%) as the effects on patients presenting beyond the 1st 20 years of the 211

time horizon are reduced due to discounting, whilst patients presenting up to 20 212

years are mostly followed up to death. 213

Analysis number 4 shows that the combined impact of reducing the time horizon and 214

including incident cases across the time horizon has a more substantial impact on 215

the estimated ICER than the sum of the parts, increasing it by over £3,000 (over 216

27%). 217

Comparisons of the same set of analyses within the 50 and 70 year old patient 218

cohorts show a similar pattern. The difference from the reference case results is 219

highest for the combined case (incident cases and short time horizon) for the cohort 220

of 50 year old patients as the shorter time horizon assumption has a greater 221

proportional impact in the case study model. The absolute effects in the 60 and 70 222

year old cohorts are similar for the shorter time horizon assumption, and the 223

percentage change differs only due to the magnitude of the reference case estimate. 224

Table 3 presents a comparison of the results within the separate age groups with the 225

results of a age-weighted aggregate patient cohort that reflects the age distribution of 226

patient cohort within the general population. Analyses numbers 1, 5, 9, and 13 show 227

that the reference case ICER (no incident cases, and a time horizon that follows 228

patients to a maximum age of 100 years) for the youngest of the three single age 229

patient cohorts was almost double the value of the ICER for the oldest age cohort, 230

and that there was a 14% difference between the age-weighted ICER and the ICER 231

for the middle age cohort (the most likely single age cohort to be presented in a singe 232


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age cohort analysis). A 60% difference in the ICER is noted when comparing the 233

reference case results for the 50 year old cohort and the age-weighted cohort. 234

Analysis number 8, involving a 20 year time horizon, incident cases and a 50 year old 235

cohort increases the ICER by over 120% relative to the reference case age-weighted 236

analysis. Comparing the age-weighted cohort with the 60 year old cohort, analysis 237

number 12 shows that if incident cases were combined with a shorter time horizon, 238

the estimated ICER would be 45% higher than the age-weighted reference case. 239

Discussion 240

This paper explores the impact of implementing alternative modeling assumptions 241

relating to the inclusion of incident cases over the course of the specified time 242

horizon; the use of a non-lifetime time horizon; and the handling of age-specific 243

patient sub-groups. The analysis of a published model that incorporated significant 244

age-specific parameter values shows that the effect of assumptions alternative to 245

defined reference case scenarios can have a significant impact on the resulting cost-246

effectiveness ratios. 247

The case study evaluation involves relatively high levels of disease-specific mortality. 248

This would be expected to reduce the estimated impact of including incident cases 249

and short time horizons as fewer long-term costs and effects are omitted. Other 250

models in disease areas with lower mortality effects would be expected to identify 251

even more significant differences in the incremental cost per QALY gained estimated 252

by alternative sets of assumptions. 253

Philips et al,[1] in their review of guidelines for good practice in decision-analytic 254

modeling in health technology assessment, state that existing guidelines (including 255

their own amalgamated guideline) require further development, but that guidelines 256

are likely to be ‘too general to pick up on the specific nuances of each model’ (pg iii). 257

These guidelines identify the appropriate time horizon as one that extends ‘far 258


13

enough into the future to reflect important differences between options’ (pg21), and 259

recognise that a lifetime horizon is the most relevant option for most models.[1] 260

Weinstein et al also state that “Lifetime horizons are appropriate for many models 261

and are almost always required for models in which options have different 262

timevarying survival rates”[pg12][15] We have identified various model-based 263

evaluations that have not adopted these recommendations,[3-5] a factor that reduces 264

the comparability of published evaluations and may contribute to flawed policy 265

decisions. 266

The issue of modeling incident cases over the duration of a model is not explicitly 267

addressed by current guidelines. It is linked to the time horizon of the model as all 268

important effects of an intervention for each patient included in a model should be 269

described, i.e. all patients entering the model should be followed for their lifetime. 270

Few cost-effectiveness models have included incident cases to the end of the 271

specified time horizon, as done by Gaspoz et al,[3] though this study serves to clarify 272

the erroneous nature of this approach and to demonstrate the potential effects on the 273

cost-effectiveness results. 274

The definition of the appropriate patient cohort, specifically with regard to the age 275

distribution of an eligible patient population is important. Many model-based 276

treatment evaluations do not include any age-specific data other than general 277

population (non-disease specific) mortality rates,[16,17] which may reduce the impact 278

of modelling separate age cohorts. However, in cases in which more widespread 279

age-specific data are available the impact on aggregate cost-effectiveness may be 280

significant. 281

The fourth issue raised in this paper concerns the parsimony of decision analytic 282

models for the cost-effectiveness analysis of health care interventions, as 283

recommended by Weinstein et al.[15] The model that highlighted this issue was an 284

application of the CHDP model for the cost-effectiveness analysis of antiplatelet 285


14

therapies.[3] This initial publication of this model was amended due to the post-286

publication identification of modelling errors, and the amended version retained some 287

seemingly counter-intuitive results that were not addressed by the authors. Though it 288

is not possible to prove a causal link between a non-parsimonious model and model 289

errors, we believe this example does confirm the value of model parsimony. 290

The potential advantage of using a broad model such as the CHDP model, to 291

evaluate a range of alternative interventions in the same broad disease area is that it 292

may provide a more suitable basis for the comparison of their relative cost-293

effectiveness. There is a balance to be struck regarding the benefits derived from 294

increased comparability of different evaluations and the extent to which reduced 295

parsimony complicates the analysis and increases the probability of error. 296

The increased awareness of the economic aspects of health care, and the need for a 297

guided rationale in allocating scarce health care resources that provides explicit 298

reasons for the choices made, led to a rapid increase in the number of published 299

economic evaluations of health care technologies over the last decade. Though 300

there is a growing acceptance of the role for economic evaluation in informing clinical 301

and policy decisions over the provision of alternative technologies, there remains 302

some suspicion around the methodological validity of economic evaluations. 303

Critics believe that the results of model-based evaluations can be manipulated more 304

easily and with more subtlety than other forms of evaluation,[18] and that the ‘black 305

box’ feel about the analysis of such models does not enhance confidence in the 306

derived results.[19] Such criticisms have less foundation when a transparent 307

description of model assumptions and data sources is provided. Halpern et al accept 308

that there are problems associated with modeling studies, but argue that many of the 309

cited problems could be countered through 'rigorous peer review and methodological 310

development'.[20] 311


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The main limitation of this paper is that the effects of three of the identified issues 312

were only examined in a single case study model, and so the study is not able to 313

generalise the expected magnitude of the observed effects across a range of case 314

studies in different disease areas. It was also not feasible to undertake a quantitative 315

analysis of the effects of model parsimony, which in our view, would require a multi-316

centre study, in which separately developed models are subject to blinded review by 317

independent expert reviewers. 318

This paper has demonstrated the effects of three specific issues to the extent that 319

they are important issues that have the potential to significantly affect cost-320

effectiveness results and that they should now be included in modelling guidelines. 321

The continued development, dissemination, and application of guidelines for model-322

based economic evaluations is necessary to further enable evidence based decision 323

making. 324


16

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for carotid stenosis in asymptomatic patients. Ann Intern Med 1997;126:337-

46.

31 Tengs TO, Lin TH. A meta-analysis of quality of life estimates for stroke.

Pharmacoeconomics 2003;21:191-200.


20

Figure legends Figure 1 Decision tree model structure describing possible patient pathways

over the 1-year treatment period

ACS=acute coronary syndromes; CABG=coronary artery bypass graft;

MI=myocardial infarction; PCI=percutaneous coronary intervention.

Figure 2 Markov model structure describing possible patient pathways over the

remainder of the patient cohort’s lifetime

Table 1 Relative risks, costs and utility values used in the model

Table 2 Comparison of incident cases and time horizon assumptions in a

single age patient cohort (60 years olds)

Table 3 Comparison of single age and age-weighted patient cohort analyses


21

Figure 1

12 months revasc. PCI

12 months revasc. CABG

Repeat revasc.

(no repeat rev asc.)

Acute PCI

Acute CABG

12 months revasc. PCI

12 months revasc. CABG

12 month revasc.

Death

MI

Stroke

Event-f ree

1

(no revasc.)

(no acute CABG)

(no acute PCI)

ACS patients

Go to 1

Go to 1

Go to 1


22

Figure 2

ACSEvent-free

MIYear 1

StrokeYear 1

StrokePost-Year 1

MIPost-Year 1

Dead


23

Table 1

Relative risk Mean Source

Non-fatal MI (clopidogrel+standard therapy vs standard therapy)

0.71 14

Non-fatal stroke (clopidogrel+standard therapy vs standard therapy)

0.73 14

Vascular death (clopidogrel+standard therapy vs standard therapy)

0.93 14

Revascularisation (clopidogrel+standard therapy vs standard therapy)

0.976 14

Major bleed (clopidogrel+standard therapy vs standard therapy)

1.38 14

Cost (£s)

ASA 300 mg/day (pa) 3.47 21 Clopidogrel 75 mg/day (pa) 460.32 21 PCI revascularisation 2,455 22 (HRG E15) CABG revascularisation 6,275 22 (HRG E05) Major bleeding event 2,064 23 ACS event-free (year 1) 1,421 16 ACS event-free (post-year 1) 1,421 16 MI (year 1) 3,966 16 MI (post-year 1) 1,587 16 Stroke (year 1)* 7,466 24 Utility values

ACS event-free (year 1) 0.80 25-27 ACS event-free (post-year 1) 0.93 28-30 MI (year 1) 0.80 25-27 MI (post-year 1) 0.93 28-30 Stroke (year 1)* 0.69 31 *Based on 35% of new stroke patients being dependent.[24] No utility values are described for the post-year 1 stroke state because the model assumes patients experiencing a stroke return to the ACS event-free state due to a lack of relevant prognostic data for these stroke patients. ACS=acute coronary syndromes; ASA=acetylsalicyclic acid; CABG=coronary artery bypass graft; CI=confidence interval; MI=myocardial infarction; PCI=percutaneous coronary intervention.


24

Table 2

Costs QALYs

Framework parameters ASA ASA+Clopidogrel ASA ASA+Clopidogrel ICER % difference from

reference case

1 40 year time horizon, no incident cases £15,604 £15,997 8.843 8.879 £11,054 - 2 20 year time horizon, no incident cases £14,558 £14,952 8.212 8.248 £11,065 0.10% 3 40 year time horizon, incident cases £7,685 £7,901 4.320 4.339 £11,860 7.3% 4 20 year time horizon, incident cases £8,007 £8,291 4.407 4.427 £14,085 27.4%

ASA – aspirin; ICER – incremental cost-effectiveness ratio


25

Table 3

Costs QALYs Framework parameters

ASA ASA+Clopidogrel ASA ASA+Clopidogrel ICER % difference from

reference case

Weighted aggregate cohort

1 30-50 year time horizon, no incident cases £13,543 £13,936 7.574 7.614 £9,716 - 2 20 year time horizon, no incident cases £12,700 £13,093 7.063 7.104 £9,748 0.3% 3 30-50 year time horizon, incident cases £6,953 £7,182 3.840 3.862 £10,380 6.8% 4 20 year time horizon, incident cases £7,219 £7,501 3.914 3.937 £11,946 23.0%

50 years

5 50 year time horizon, no incident cases £21,296 £21,687 12.375 12.400 £15,630 60.9% 6 20 year time horizon, no incident cases £17,944 £18,337 10.342 10.367 £15,919 63.8% 7 50 year time horizon, incident cases £9,264 £9,454 5.355 5.367 £16,387 68.7% 8 20 year time horizon, incident cases £9,240 £9,525 5.199 5.212 £21,533 121.6%

60 years

9 40 year time horizon, no incident cases £15,604 £15,997 8.843 8.879 £11,054 13.8% 10 20 year time horizon, no incident cases £14,558 £14,952 8.212 8.248 £11,065 13.9% 11 40 year time horizon, incident cases £7,685 £7,901 4.320 4.339 £11,860 22.1% 12 20 year time horizon, incident cases £8,007 £8,291 4.407 4.427 £14,085 45.0%

70 years

13 20 year time horizon, incident cases £6,300 £6,581 3.334 3.362 £10,011 3.0% 14 30 year time horizon, incident cases £5,995 £6,240 3.212 3.238 £9,197 5.3% 15 20 year time horizon, no incident cases £10,417 £10,809 5.643 5.690 £8,408 13.5% 16 30 year time horizon, no incident cases £10,544 £10,936 5.719 5.766 £8,393 13.6%

ASA – aspirin; ICER – incremental cost-effectiveness ratio

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