REVIEW ARTICLE

Model-Based Cost-Effectiveness Analyses for the Treatmentof Chronic Myeloid Leukaemia: A Review and Summaryof Challenges

Kevin Marsh • Peng Xu • Panagiotis Orfanos •

Agnes Benedict • Kamal Desai • Ingolf Griebsch

� Springer International Publishing Switzerland 2014

Abstract Assessing the economic value of treatments for

chronic myeloid leukaemia (CML) is important but poses a

number of challenges. This paper reviews economic

models of CML treatment to learn lessons from this

experience and support ongoing efforts to model CML. A

search of databases and submissions to key health tech-

nology assessment agencies identified 12 studies that

reported 22 models. Common practice included the use of

cohort Markov models—most models used health states

organised around the key stages in CML: chronic phase,

accelerated phase and blast phase—and the use of utility

estimates in the literature that correspond with the National

Institute for Health and Care Excellence reference case.

Two key areas of uncertainty were the extrapolation of

survival outcomes beyond the period observed by the trial;

and the effectiveness of second-line therapies. Further

work is required to overcome these uncertainties in existing

models, such as longer-term trial data collection, including

trials of second-line therapies; validation of health-related

quality-of-life instruments; and the testing of alternative

modelling approaches. In the meantime, it is important that

the impact of uncertainties is tested through the use of

sensitivity and scenario analysis.

Key Points for Decision Makers

The short periods observed by clinical trials mean

that it is necessary to use modelling techniques to

assess the lifetime economic value of chronic

myeloid leukaemia (CML) treatments.

The modelling approaches adopted to date have been

generally regarded as acceptable by decision makers,

but still require significant structural assumptions.

Decision makers should consider the validation of

models, including structural sensitivity analysis, to

test the impact of any assumptions.

Further research should focus on the collection of

longer-term trial data on both first- and second-line

therapies, and the validation of instruments for

measuring health-related quality of life.

1 Introduction

Chronic myeloid leukaemia (CML) represents 7–20 % of all

leukaemia cases, with a worldwide incidence of one to two per

100,000 population in 2004 [1–3] and an age-adjusted mortality

rate of 1.0 per 100,000 [3]. The median survival for patients

with untreated CML is 4–5 years [4]. The natural history of

Electronic supplementary material The online version of thisarticle (doi:10.1007/s40273-014-0177-3) contains supplementarymaterial, which is available to authorized users.

Peng Xu was an employee of Evidera until submission of the

manuscript

K. Marsh (&) � P. Xu � P. Orfanos � K. Desai

Evidera, Metro Building, 6th Floor, 1 Butterwick,

London W6 8DL, UK

e-mail: kevin.marsh@evidera.com

A. Benedict

Evidera, Beg u. 3-5/520, 1022 Budapest, Hungary

I. Griebsch

Boehringer Ingelheim Pharma GmbH, Ingelheim am Rhein,

Germany

PharmacoEconomics

DOI 10.1007/s40273-014-0177-3

CML can be divided into three phases: a stable or chronic phase

(CP), an accelerated phase (AP) and a blast phase (BP). The

majority (80–90 %) of CML patients are diagnosed in the CP

(with a median age at diagnosis of 65 years), and up to 40 % are

asymptomatic [3, 5]. Without treatment, patients will progress

from CP to AP within 3–5 years, while often still asymptom-

atic, and from AP to BP within 4–6 months [6–8]. Median

survival in BP is 3–6 months [5].

Before the introduction of tyrosine kinase inhibitors

(TKIs), such as imatinib, dasatinib and nilotinib, treatment

for CML largely consisted of bone marrow transplantation or

interferon and/or chemotherapy, which are associated with

severe side effects [9]. Imatinib (400 mg daily) is licensed as

a first-line therapy for patients in CP and as second-line

therapy for patients in AP or BP after failure of interferon-

alpha therapy. It is considered one of the most successful

targeted therapies developed in cancer [10], achieving com-

plete cytogenetic response (CCyR) rates of 82 % in CP [11–

13]. After the approval of imatinib as a CML treatment by the

US FDA in 2001, 5-year survival increased from 27.1 % in

1990–1992 to 48.7 % in 2002–2004 [13, 14]. More recently,

two other TKIs have improved patients’ outcomes even fur-

ther: trials of dasatinib and nilotinib used in first-line treat-

ment suggest they produce a better cytogenetic response than

imatinib [11–13]. A number of newer treatments for CML are

becoming available. In 2012, the FDA approved two TKIs,

bosutinib and ponatinib, and a protein translation inhibitor,

omacetaxine [10].

Despite these benefits, there are concerns about the costs

of CML treatments. The TKIs approved for CML have

annual prices ranging from $US92,000 to $US138,000 in

the USA [10]. This has caused some experts to argue that

the prices of TKIs are too high [10]. Therefore, assessing

the economic value of these treatments becomes critical to

weigh the cost of therapies against their health benefits. But

such assessments face a number of challenges. Following

the requirements of the National Institute for Health and

Care Excellence (NICE), an influential decision-maker,

this study focuses on two such challenges: that technolo-

gies are assessed over patients’ lifetimes and in terms of

health-related quality of life (utility) [15].

Outcomes such as survival are often only experienced by a

small proportion of patients within the period of a trial. Thus,

a lifetime perspective requires modelling to extrapolate

beyond the trial period, something that is rarely straightfor-

ward, with different methods leading to different results [16].

However, this element of health economic analysis of cancer

treatments is often done poorly, with insufficient testing and

justification of the survival model chosen [17]. It is not a

surprise then that such extrapolations have been identified as

a key source of uncertainty in models of CML [18].

Furthermore, to facilitate the comparison of the benefit

of technologies across therapy areas, NICE requires that

standardised utility measures are employed. Specifically,

NICE’s preferred approach is to use the EQ-5D, but will

accept alternative approaches where the EQ-5D can be

demonstrated to be inappropriate [15]. However, the use of

the EQ-5D to measure CML outcomes is subject to limi-

tations [9]: it is not as sensitive to disease-specific impacts

as direct methods like the time trade-off (TTO) approach,

as it focuses on current health status; it cannot explicitly

measure key psychological effects such as knowing that

one is responding to treatment; and it cannot control for the

idiosyncratic variation that can be substantial in cancer.

Similar arguments are made against the use of the EQ-5D

to measure cancer health states more generally—that it

does not capture health outcomes such as vitality, which

are particularly important for cancers [19].

Given these challenges, it is not surprising to discover

that NICE’s assessment of the cost-effectiveness analysis

(CEA) of TKIs has produced mixed results: standard dose

imatinib (400 mg) was recommended for reimbursement as

a first-line treatment for CML [20]; and nilotinib was

recommended for reimbursement as first- and second-line

treatment [11, 20]. However, high-dose imatinib

(600–800 mg in CP or 800 mg in AP or BP) was not

recommended as a second-line treatment for CML in

populations resistant to standard-dose imatinib [11–13],

and dasatinib was not recommended for reimbursement as

first- or second-line treatment [11, 21].

The objective of this paper is to support ongoing efforts

to build economic models of CML by reviewing the

approaches to extrapolating outcomes and estimating util-

ity adopted to date and the lessons learned from this

experience.

2 Methods

Embase, MEDLINE, Cochrane library, National Health

Service Economic Evaluation Database (NHS EED) and

NHS Health Economic Evaluation Database (HEED) were

searched on 15 May 2013. The websites of health tech-

nology assessment (HTA) agencies were searched,

including: NICE, Scottish Medical Consortium (SMC),

Agency for Healthcare Research and quality (AHRQ),

Pharmaceutical Benefits Advisory Committee (PBAC),

Canadian Agency for Drugs and Technologies in Health

(CADTH), College voor zorgverzekeringen—Health Care

Insurance Board (CVZ) and the Dental and Pharmaceutical

Benefits Agency in Sweden (TLV). The search strings are

reported in the Electronic Supplementary Material (ESM)

online resource 1. The total number of hits after removing

duplicates was 410. After a review of titles and abstracts,

35 studies were retrieved for full-text review, following

which, 12 studies were included in the final review and

K. Marsh et al.

analyses. The Preferred Reporting Items for Systematic

Reviews and Meta-Analyses (PRISMA) diagram is repor-

ted in ESM 2.

Studies were included if they reported an economic

model to estimate the cost effectiveness of a treatment for

CML. Studies were excluded if they (1) reported a budget

impact or cost-of-illness analysis; (2) did not report either

resource use implications or standardised health outcomes

such as quality-adjusted life-years (QALYs); (3) did not

involve the use of modelling techniques, such as economic

evaluations, alongside trials; and (4) were published in a

language other than English. Models were also excluded if

they reported a model that had already been included in the

review. No limits were placed on the location or date of

publication.

Data were extracted on study characteristics (year;

author; country; type of intervention assessed, including

line of treatment; age of participants); model characteristics

(model type, time horizon, health states, treatment

sequences); method for extrapolating beyond the trial data

(extrapolation distribution, data source and validity tests);

method for estimating health-related quality of life; base-

line cost-effectiveness result; and comments from the

authors on advantages and disadvantages of the model

approach employed. Data were extracted independently by

two reviewers. Disagreements were resolved through dis-

cussion between the reviewers and a third party.1

Two sets of analyses were undertaken. First, descriptive

statistics were generated on the frequency of different

approaches. Second, authors’ comments on modelling

methods were organised into themes and synthesised.

3 Results

3.1 Study Characteristics

The search identified 12 studies (Table 1), which reported

22 separate models. Some studies reported more than one

model, as different models were constructed for the dif-

ferent treatment arms—for instance, where a pharmaceu-

tical intervention was compared against stem cell

transplantation (SCT); or the study was part of a multiple

technology assessment for an HTA agency. Two studies

were submissions to HTA agencies and contained nine

models. The studies were published between 1996 and

2012, with nearly half the models (n = 10) being published

in 2012. Most models were undertaken in the UK (n = 12),

assessed pharmaceutical interventions (n = 19), and

assessed first-line treatment (n = 12).

3.2 Model Characteristics

All but one of the models (n = 21) adopted a cohort

Markov model approach, with the exception using a deci-

sion tree. In the small proportion of studies where the

modelling approach was discussed, it was agreed that the

Markov structure was an appropriate approach for model-

ling CML, reflecting patient progression through the phases

of the disease [18, 22, 23].

All the Markov models organised health states around

the phases of CML disease progression: CP, AP, BP, and

death. Two types of model structure predominated across

the studies: first, models in which all patients (regardless of

whether they respond to treatment) had the same proba-

bility of transition between these states (Fig. 1a); second,

models that distinguished whether those in CP had

responded to treatment, applying different transition

probabilities to responders and non-responders (Fig. 1b).

3.3 Extrapolation Methods

A major source of uncertainty in CML models is the imma-

turity of the trial evidence, which is invariably significantly

shorter than the period of survival, requiring extrapolation if a

lifetime assessment is to be undertaken [22]. Two approaches

were employed in response to this uncertainty. Most studies

extrapolated beyond the period observed in the trial (n = 16).

A minority of studies limited the analysis to the trial period

(n = 6). The primary reason given for adopting a shorter

timeline was the level of uncertainty associated with longer

time horizons due to the lack of long-term data on which to

base a model [24–27]. Those that adopted longer-term time

horizons also acknowledged the high level of uncertainty

associated with extrapolating over such long periods [18, 22].

The impact of this uncertainty was demonstrated by a study

that repeated an analysis based on extrapolations derived

from 19- and 60-month cuts of the same data. The extrapo-

lation based on 19-month data was estimated to have under-

estimated mean survival for patients receiving imatinib by

almost 4 years [28].

3.3.1 ‘Direct’ and ‘Surrogate’ Modelling Approaches

Two different approaches were used to model survival (Fig. 1a,

b): (1) fitting distributions to survival data in the trial data—the

‘direct approach’ (n = 6); and (2) using trial data to estimate

response, such as complete haematological response (CHR) or

major cytogenetic response (MCyR) and projecting based on

the relationship between response and survival derived from

other sources—the ‘surrogate approach’ (n = 15).2

1 Only three disagreements were identified, all relating to the method

used to generate extrapolation distributions.

2 The model from Gaultney et al. [25] did not model survival and is

thus not included in this classification.

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K. Marsh et al.

Study authors identified a number of benefits with the

surrogate approach. First, response rates are statistically

well established as a surrogate measure of survival [18].

Second, the direct estimate of the impact of treatment on

overall survival (OS) is often confounded by co-interven-

tion and subsequent treatments. That is, it is not possible to

isolate the impact of a treatment on survival using data

from trials in which treatment failure results in access to

alternative treatments. The indirect approach overcomes

this challenge, as the trial data are only used to estimate

treatment response, which coincides with treatment failure

and, thus, are not confounded by subsequent lines of

treatment available to trial participants [18]. Third, as the

relationship between response and OS is drawn from data

sources other than the trial, they can draw on longer-term

observations, such as the IRIS (International Randomized

Interferon versus STI571) trial data.

The IRIS trial data was the source most used in the

models (it was used to generate 19 extrapolations of pro-

gression or survival in 11 models). IRIS was first con-

ducted in 2002 and followed 237 imatinib patients for a

median of 8.5 months [29]. In 2003, the IRIS trial sample

size increased to 1,106 (553 on imatinib and 553 on

interferon and low-dose cytarabine), with a median follow-

up period of 19 months [30]. In the most recent iteration of

IRIS results, the median follow-up period was 60 months

[31], with a maximum follow-up period of up to 8 years

[18].

Other data used in the studies were generally of shorter

duration than IRIS:

• The interferon alpha (IFN-a) trial (used to inform seven

distributions across three models), which began in 1994

with 218 patients in the interferon arm and 104 in the

Fig. 1 a An example of a

model structure that did not

distinguish patients based on

where they respond to treatment

(adapted from Rogers et al.

[18]; the authors assumed that

patients had to move through all

stages of CML before they

would experience CML-related

mortality). CML chronic

myeloid leukaemia, CP chronic

phase. b An example of a model

structure that did distinguish

patients based on treatment

response. The rate at which

individuals will progress

through the model will depend

on their response to treatment

(no response, complete

haematological response, partial

cytogenetic response or

complete cytogenetic response)

Model-Based CEAs for the Treatment of Chronic Myeloid Leukaemia

conventional chemotherapy arm, and provided data for

a median of 72 months for interferon and for

52 months in the comparator arm [32].

• The dasatinib trial (used to inform ten distributions

across four models) has a median follow-up of

15 months, and includes 101 patients on dasatinib and

49 on imatinib [33].

• The nilotinib trial (ENEST, used to inform three

distributions in two models) had a median follow-up

of 12 months, and includes 281 and 282 patients on

nilotinib 400 and 300 mg, respectively, and 283

patients on imatinib [34].

• The IBMTR (International Bone and Marrow Trans-

plant Research) database (used to inform two distribu-

tions across two models) based on two studies of SCT

patients.

• The EBM (European Group for Blood and Marrow

transplantation) study (used to inform four distributions

across three models), which followed 44 patients on

imatinib after SCT [35].

However, the authors of the studies also identified a

number of sources of uncertainty with the surrogate

approach. First, as noted above, to take advantage of the

longer-term datasets, the relationship between response and

OS is often estimated from the IRIS data. Patients in IRIS

receive either imatinib or interferon. Thus, using the rela-

tionship between response and survival estimated based on

IRIS when modelling treatments other than imatinib or

interferon requires the assumption that the relationship

between response and survival is independent of the

treatment being evaluated [18]. This assumption may be

reasonable, as there is evidence from the IRIS data that the

relationship between MCyR and OS is similar for those

treated with imatinib or interferon [18].

Second, the different definitions of response adopted

across studies mean that it is not always possible to com-

bine analyses undertaken on different data in the manner

required by the surrogate approach [18]. Different mea-

sures of response may be adopted across studies, including

CHR or MCyR. If a surrogate approach is to be adopted, it

is important that trials are designed to include the same

response variables as the other data that will be used to

extrapolate beyond the trial.

Using data on loss of response, such as loss of CHR or

loss of MCyR, to model progression from CP to AP is

another source of uncertainty [18]. This is often estimated

by fitting a distribution to the trial data to extrapolate

progression-free survival (PFS). However, the resulting

progression rate may not be accurate when PFS is defined

in the data as including loss of CHR or loss of MCyR,

rather than just transformation to AP/BP. As people can

spend several years in CP following loss of MCyR,

modelling progression to AP based on this definition of

response would underestimate the time in CP and, thus,

OS. An alternative approach was to estimate time in CP by

predicting OS and adjusting for time expected in AP and

BP [22].

Third, the surrogate approach often adopts a relatively

simple relationship between response and OS, distin-

guishing OS for a binary yes/no measure of response—

above and below a level of response [18, 22, 26]. This

ignores the differential impact of various depths of

response, for example, the different survival implications

of achieving partial CyR or complete molecular response

[18]. There is evidence that would support a more nuanced

approach—that deeper response predicts a longer response

[18] and may be associated with greater PFS [18]. How-

ever, this evidence falls short of demonstrating that depth

of response converts into greater OS [18].

3.3.2 Modelling Second-Line Therapy

Another source of uncertainty in extrapolations is the

effectiveness of second-line therapy. The majority of

studies (n = 15) include treatment switching. This is the

case for models of both first line (n = 8) and second line

(n = 7) of treatment. Most of the studies (n = 11) that

allowed switching to second line did so on disease pro-

gression, such as the movement from CP to AP. A few

models allowed patients to switch to second-line treatment

within the same health state (n = 4). Three of these studies

required that switching of treatment occurred in CP before

the disease progressed to AP (the Novartis and PenTAG

models reported in Pavey et al. [22]). That is, patients

would have to fail first-line treatment and switch treatment

before their disease progressed, and could not transition to

AP while on first-line treatments. The other model allowed

patients to switch treatment within CP and to transition to

AP from any of these lines of treatment (the Bristol-Myers

Squibb model reported in Pavey et al. [22]). The same

transition probability between CP and AP was applied for

different lines of treatment.

Authors identified a number of challenges facing the

modelling of treatment switching. There is uncertainty over

the heterogeneous CML treatment and care pathways—

there are many potential paths depending on how patients

respond to treatment, age, disease severity, the availability

of matched donors for SCT, and mutations that predict

responsiveness to TKIs [22]. There is also uncertainty due

to the limited clinical evidence for second-line treatments

[25]. One author reported no robust randomised evidence

on the effect of second-line therapies based on cross-over

trials, causing models of second-line therapy to rely on a

variety of observational data [18]. These data are subject to

a number of sources of uncertainty, limiting the

K. Marsh et al.

comparability of estimates of the effect of different second-

line treatments, including different entry criteria and dif-

ferent definitions of progression [18].

As with first-line treatment, there is limited direct evi-

dence of survival on second-line treatment, as few patients

die within the period of the trial. The commonly preferred

alternative extrapolation method—via surrogates such as

treatment response—requires a number of assumptions.

While IRIS is the preferred dataset to inform the projection

of OS outcomes given its long follow-up period, it does not

include second-line TKI following imatinib failure.

Therefore, studies that use these data are required to

assume that the response–survival relationship is indepen-

dent of treatment type and line.

3.3.3 Extrapolation Functions

Across the variety of transition probabilities used in the

models, a total of 45 different extrapolations were

identified in the studies. This number reflects the fact

that transitions are required for PFS and OS, in some

instance for responders and non-responders, and the fact

that some studies built separate models for the different

treatments being assessed. Although specifying differ-

ent curves for treatments based on a constant hazard

ratio was not counted as a separate example of

extrapolation.

A range of approaches were used to specify the distri-

butions for these extrapolations (Table 1). Contrary to

good practice guidelines published in 2013 [17], the dis-

tribution used was often not reported (n = 13) or the rate

of the event was assumed to be constant (n = 14) (rather

than analysing the data to determine that an exponential

distribution fit well and, thus, a constant rate would be

appropriate); although there was a trend over time away

from not reporting distributions and assuming constant

rates. In 2012, only 4 of 15 extrapolations were based on

the assumed constant rates, while nine extrapolations were

derived by fitting a distribution to trial data. Where specific

distributions were derived from the data, the most com-

monly used distributions were exponential (n = 8) and

Weibull (n = 5).

Also contrary to good practice guidelines [17], only a

minority of studies reported the statistical fit of the distri-

bution to the data (e.g. using Akaike information criterion

[AIC], Bayesian information criterion [BIC]) (n = 1) or

that the validity of the extrapolation was tested against

expert opinion or another dataset (n = 5).

No association was observed between the distribution

used and the characteristics of the model; whether OS or

PFS was being modelled, or whether survival was being

modelled directly or via a surrogate such as treatment

response.

3.4 Utility

Of the 22 studies, 19 used QALYs as an outcome measure.

The sources of utility varied between the studies, including

extracting utility values from literature (n = 9); direct

elicitation by the study authors (the valuation of health

states using techniques such as TTO and standard gamble

[SG]) (n = 6); and indirect methods applied by the study

authors (the collection of utility data in trials using generic

instruments such as the EQ-5D) (n = 4).

The methods for estimating the utility used in the studies

varied (Table 1; Table 2). Direct valuation, either by the

study authors or extracted from the literature, used either

TTO derived from patients (n = 4), or SG (n = 2) or

visual analogue scales (VAS, n = 2) derived from clini-

cians. Indirect valuation, again either by the study authors

or extracted from the literature, involved the collection of

EQ-5D from either patients (n = 9) or clinicians (n = 2).

Authors acknowledged that using clinicians to estimate

the utility of health states, either with direct or indirect

approaches, is limited by the fact that only patients with

CML know, with accuracy, what life is like with the dis-

ease [24, 26]. Reflecting this concern, once utility estimates

derived from patients became available in 2008 [36], no

study has relied on clinician-derived utility estimates.

Only seven different sources of the utility of CML

health states were used across all the studies (Table 2). A

large proportion of studies drew on the estimates generated

by Reed et al. [28] (n = 10), an indirect estimate of utility

using EQ-5D data derived from patients and general pop-

ulation tariffs. This approach corresponds with that rec-

ommended by NICE, and since it was published in 2008,

69 % of the CML models have used the utilities they

generated.

The utility in CP is lower when estimated from patient-

based EQ-5D (0.71 with interferon, [31]) than those esti-

mates based on clinician expertise (0.875–0.9), both direct

estimates using the VAS [30, 37]. The opposite is the case

for utility in AP and BP, which is higher when estimated

based on patient-based EQ-5D (0.595) than direct estimates

by clinicians (0.5). Direct estimation by the general pop-

ulation [9] produces even lower utility in CP and even

higher utility in AP (assuming a response rate of 0.82 [11]

utility in CP with imatinib would be 0.819 compared with

0.854 based on patient-based EQ-5D estimates; and utility

in AP with imatinib would be 0.738 compared with 0.595

based on patient-based EQ-5D estimates).

3.5 Impact of Methods on Study Results

The limited number of studies reviewed, and the diversity

of research problems (treatments being assessed, line of

treatment) and methodologies (different model structures,

Model-Based CEAs for the Treatment of Chronic Myeloid Leukaemia

extrapolation approaches, sources of utility data) employed

by the studies meant that it was difficult to identify trends

in how different methods impact study outcomes. How-

ever, many of the studies reviewed undertook sensitivity

analyses. Most of the studies (n = 9) reported an assessment

of the sensitivity of model results to variations in utility

estimates. However, there was no consistent conclusion on

the sensitivity of results to utility estimates. This may be

the result of authors defining model sensitivity in terms of

the impact on the conclusion of the model, meaning that

results are considered insensitive to changes in utility

estimates, even if they produce relatively large changes in

incremental cost-effectiveness ratios (ICERs) if the overall

conclusion of cost effectiveness is not impacted by this

change. For instance, Breitscheidel et al. [24] varied the

utility following SCT from the base value of 0.769–0.854,

the equivalent of a patient in CP. This caused the ICER to

increase from €52,447 to €74,600, from which the authors

concluded that the model was not very sensitive to changes

in utility estimates. However, this 42 % increase in the

ICER could have a significant impact on the conclusion of

models built in other contexts.

A number of other analyses also identified large changes

in ICERs with changes in utility inputs. For instance,

Rogers et al. [18] reduced the utility in various health states

(the utility of CP on treatment was reduced from 0.85 to

0.76; CP off treatment from 0.85 to 0.7; AP from 0.73 to

0.6; and BP from 0.52 to 0.4) and concluded that this did

not impact the assessment of nilotinib for imatinib-resistant

patients, which remained dominant. However, the same

changes to the model when applied to dasatinib caused the

ICER to vary from £99,499 to between £56,890 and

£146,879. Kattan et al. [37] observed a large range of

utility estimates for patients on interferon (0.62–1). When

this range was tested in the model, the ICER ranged from

$25,600 to $250,000, compared with an ICER of $34,800

when using the average utility estimate.

Only one-quarter of the studies (n = 3) reported

assessments of sensitivity to extrapolation estimates or

methods. For instance, Rogers et al. [18] assessed the

impact on model results of variations in post-progression

survival. They found that changing post-progression sur-

vival for nilotinib from 10.5 years to be the same as i-

matinib (9.7 years) caused the results of the analysis to

change from nilotinib being dominant to the ICER being

£113,861. A similar change to the dasatinib model, varying

the post-progression survival for dasatinib from 6.9 years

to that for imatinib (9.7 years), caused the ICER to reduce

from £91,499 to £43,174.

Pavey et al. [22] used different datasets to estimate the

time spent on first-line treatment. The base-case ICER for

first-line nilotinib was £25,000 based on time on treatment

of 8.9 years for nilotinib and 7 years for imatinib3. Using

the IRIS data to estimate time on first-line treatment of

13.8 years for nilotinib and 11.7 years for imatinib caused

the ICER to reduce to £14,000. A similar sensitivity ana-

lysis for the model applied to dasatinib caused the ICER to

increase from £414,000 to £565,000.

3 Based on model scenario 1 by Pavey et al. [22].

Table 2 Sources of utility used in the models

References Year Utility

method

Source Mean utility data used in the model

CP AP BP

Gordois

et al. [26]

2003 Indirect

(EQ-5D)

Clinicians (n = 6) 0.91 (imatinib) 0.58 (imatinib)/0.34

(palliative care)

0.38 (imatinib)

Kattan

et al. [37]

1996 Direct

(VASa)

Clinicians (n = NR) 0.9a (interferon)/1.0

(hydroxyurea)/0.95 (with

BMT)

0.5 0.5

Lee et al.

[42]

1997 Direct

(SGa)

Clinicians (n = 12) 0.9 (with BMT)/ 0.979 (without

BMT)

Liberato

et al. [30]

1997 Direct

(VAS)

Clinicians (n = 10) 0.875 (interferon)/0.98

(hydroxyurea)/0.94 (busulfan)

0.5

Reed et al.

[28]

2008 Indirect

(EQ-5D)

Patients from IRIS

trial (n = NR)

0.854 imatinib/0.710 interferon 0.595 0.595

Szabo et al.

[9]b2008 TTO General population

(n = 97)

0.85 (responder)/0.68 (not

responder)

0.79 (responder)/0.50

(not responder)

0.50 (responder)/0.31

(non responder)

Warren

et al. [23]

2004 Indirect

(EQ-5D)

Clinicians (n = 6) 0.9 (treatment independent) 0.34 (home palliative

care)

0.04 (home palliative

care)

AP accelerated phase, BMT bone marrow transplantation, BP blast phase, CP chronic phase, NR not reported, SG standard gamble, TTO time

trade-off, VAS visual analogue scalea The data extracted from Katten et al. [37] is referred to by the authors as ‘baseline’b TA241 [22] referenced Levy et al. [44] but actually took the utility data from Szabo et al. [9]

K. Marsh et al.

Pavey et al. [22] also undertook a comparative analysis

of the impact of modelling methods, estimating the impact

of different methods for measuring survival and different

second-line treatments. For instance, the ICER for nilotinib

followed by hydroxycarbamide compared with imatinib

followed by hydroxycarbamide was £25,000 when OS is

estimated using the direct approach, £40,000 when OS is

estimated based on the relationship between major

molecular response (MMR) at 12 months and survival, and

£19,000 when OS is estimated based on the relationship

between MMR CCyR at 12 months and survival.

The ICER was more sensitive to changes in second-line

treatment. For instance, when first-line imatinib and nil-

otinib are both followed by second-line hydroxycarbamide,

nilotinib generates more QALYs (9.4) than imatinib (9.0),

with an ICER of £25,000. However, when the second-line

treatment following failure of imatinib is changed from

hydroxycarbamide to nilotinib, imatinib generates more

QALYs (9.5) than nilotinib, with an ICER of £192,000.

Pavey et al. [22] concluded from this structural sensi-

tivity analysis that ‘‘the variation in cost effectiveness

results across the […] scenarios is considerable’’ [p. 100]

and that the results ‘‘reinforce the significance of structural

uncertainty in the modelling of CML, including the sub-

stantial impact of assumptions regarding second- and third-

line treatment sequences’’ [p. 101].

4 Discussion

The objective of this paper is to support ongoing efforts to

build economic models of CML by reviewing the model-

ling approaches adopted to date and the lessons learned

from this experience, focusing specifically on methods for

extrapolating beyond the period observed by a trial and for

estimating utility. The review identified 12 studies that

included 22 different economic models of CML. A number

of trends emerge from this literature that can usefully

inform the modelling of CML.

First, while cohort Markov models with health states

corresponding to the phases of the development of CML—

CP, AP, BP—are generally regarded as an appropriate

technique for modelling CML, a number of key areas of

uncertainty are associated with these models. These

uncertainties have been referred to as ‘‘substantial struc-

tural assumptions’’ and have been demonstrated to have

significant impacts on the results of CEA [18].

A key challenge facing models of CML is the need to

extrapolate outcomes from relatively immature trial data

(1–2 years) over patients’ lifetimes (up to 40 or 50 years).

A number of recommendations can be made about how to

respond to this challenge. There was a preference among

study authors for modelling OS via surrogate measures of

response to treatment, as this approach allows the use of

longer-term observations from studies such as the IRIS

trial, and there is evidence that response is a good predictor

of survival [18]. However, authors also acknowledge that it

is not plausible to demonstrate that this is the most valid

approach, and there are concerns about the ability to gen-

eralise surrogate relationships between patients on different

treatments [15, 17, 38]. It is thus important that structural

sensitivity analysis is undertaken to understand the impli-

cations of this uncertainty. However, only one study

reported having undertaken such analysis [22]. These

uncertainties also point to the need for further research.

Given that the best source of surrogate relationships is a

study of first-line imatinib [31], this approach often

involves the assumption that the relationship between

surrogate and survival is independent of treatment type or

line of treatment. There is some evidence to support this

assumption [18], though further testing and data collection

is still required. It is also important that extrapolation

methods and their reporting are improved. The chosen

extrapolation method was often not reported or systemati-

cally justified in the studies reviewed. Greater testing of the

validity of extrapolation methods, the reporting of these

data, and testing of the implications of different extrapo-

lation approaches for model outcomes are required to

improve the confidence in the results of CEA, and to

inform modelling practice going forward. This should

correspond with best practice recommendations more

generally [16, 17, 38, 39], including the consideration of a

broad range of distributions and the selection of preferred

models based on goodness of fit over the observed period

and the plausibility of projections in the unobserved period

[16].

Further work is also required to reduce the uncertainties

associated with second-line treatment for CML. Identifying

second-line treatment will become easier as treatment

guidance becomes available. However, there will still be

important uncertainties in models of second-line treatment

due to the heterogeneity of treatment and care pathways

and the lack of good data on the effectiveness of second-

line treatment. Further data collection is recommended to

estimate the effectiveness of second-line treatments,

including SCT.

Second, uncertainties with existing approaches point to

the potential for modelling approaches other than cohort

Markov modelling. To date, surrogates have been incor-

porated into models in a simplistic manner, with survival

being predicted based on a binary measure of response at a

specific point in time. There is evidence that the timing,

duration, and depth of response will influence survival

outcomes. Individual-level modelling approaches would

facilitate the introduction of this more nuanced impact of

response into cost-effectiveness models, as well as

Model-Based CEAs for the Treatment of Chronic Myeloid Leukaemia

facilitating the modelling of patient heterogeneity, such as

variation in second-line treatment. However, before such

models are feasible, further data collection and analysis is

required to understand these relationships.

Third, the literature contains estimates of the utility of

CML that correspond with NICE guidelines, but further

work is required to determine whether this approach is the

appropriate method to estimate the utility of CML patients.

Early models of CML were criticised for relying on clini-

cians to estimate the utility of CML patients. More recent

models have been able to draw on more credible estimates

of utility data based on patient-based EQ-5D measures in

IRIS [28, 36]. However, there are a number of concerns

with the validity of these utility estimates. First, EQ-5D is

thought to exclude some health impacts of diseases, such as

fatigue or vitality, that are particularly important when

considering the impact of cancer [19] and it cannot capture

the psychological effects such as knowing one is

responding to treatment, as it focuses on current health

status [9]. The development of the EQ-5D-5L is unlikely to

overcome this concern, as this will provide more levels to

measure the same dimensions of health already captured by

the EQ-5D-3L, rather than capturing dimensions of impact

of health not currently captured by the EQ-5D-3L. How-

ever, the development of a new generic cancer quality of

life instrument (European Organisation for Research and

Treatment of Cancer [EORTC]-5D) may help to overcome

these challenges [40]. Second, IRIS may not be represen-

tative of real-world CML patients due to the restrictions of

trial inclusion criteria, and because it includes only small

samples of patients in AP and BP [9].

In situations where the EQ-5D is not considered

appropriate, agencies such as NICE will accept utility data

derived using other methods, such as the TTO direct val-

uation technique. CML utility estimates are available using

this approach [9], distinguishing not only the phases of

CML but also response and non-response. However, these

data may result in lower utility gains with CML treatment,

as the utility estimates for CP is lower than its EQ-5D-

based equivalent, and that for AP is higher. Further work is

required to determine the appropriate method to estimate

the utility of CML patients.

This paper faces a number of important limitations.

First, the objectives, methods, and reporting of the

studies vary, limiting the possibility of understanding the

implications of any particular approach for outcomes

such as survival or the ICER. For instance, the impact of

a particular survival curve on these outcomes will be

confounded by the line of treatment being assessed, the

dataset to which the distribution is fitted, and whether

survival is modelled directly or via a surrogate. Second,

it was not always possible to access full papers on the

models included in the review. In particular, seven

models were developed for submission to NICE as part

of Multiple Technology Appraisals. The manufacturer’s

reports of these models were not available, so the review

extracted information on the models from the detailed

description provided by the Evidence Review Group’s

reports [18, 22]. Third, there is limited space within

peer-review journal papers for sufficient explanation of

the methods chosen, or discussion of the method’s per-

formance. Fourth, the methodology involved the review

of existing modelling studies. In correspondence with

good modelling practice [41], an assessment of the

validity of a model should be based on a thorough

conceptual understanding of the natural history of the

disease, how treatment impacts this, and how these vary

between patients, but this is beyond the scope of this

paper.

In conclusion, a number of recommendations for

ongoing efforts to model CML can be developed from the

existing literature:

1. The collection of trial data should be planned as far in

advance as possible so that the period of observation is

as long as possible, and should be designed to allow an

analysis of the impact of second-line treatment. To

facilitate the collection of long-term data, trials should

also be planned to include open-label follow-up

period.

2. Both a surrogate and a ‘direct’ approach to extrapo-

lating OS and PFS should be adopted to explore the

uncertainties in the current knowledge about long-term

outcomes for patients with CML. The use of surrogates

should be informed by clinical opinion to identify the

most appropriate surrogates and to understand the

impact of the selection of a particular surrogate on the

results of the extrapolation.

3. Regardless of the extrapolation approach adopted,

different extrapolation functions should be tested for

their fit to the observed data and their clinical validity,

and the impact of different functions should be

reported as part of a sensitivity analysis.

4. It is recommended that a direct valuation approach is

adopted to estimating the utility of CML health states,

but that the impact of valuing health states using the

EQ-5D is also reported.

Acknowledgments This study was funded by Boehringer Ingelheim

in Germany.

Conflict of interest The authors declare that they have no conflicts

of interest to disclose regarding this study. One of the authors (Ingolf

Griebsch) is employed by Boehringer Ingelheim (BI). BI does not

have any products for CML either on the market or in development.

The funding for the review was provided to Evidera by BI. Evidera

regularly consult for BI on a range of health economics and outcomes

research projects.

K. Marsh et al.

Author contributions All authors were involved in the study

concept and design, interpretation of data, and critical revision of the

manuscript for important intellectual content. Kevin Marsh, Peng Xu

and Panos Orfanos were responsible for the acquisition and analysis

of data, and the drafting of the manuscript. Kevin Marsh supervised

all the study and acts as guarantor for the content of the paper.

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