Mapping health-related quality of life scores from FACT-G, FAACT, and FACIT-F onto preference-based EQ-5D-5L utilities in non-small cell lung cancer cachexia
Mapping health‑related quality of life scores from FACT‑G, FAACT, and FACIT‑F onto preference‑based EQ‑5D‑5L utilities in non‑small cell lung cancer cachexia
Michela Meregaglia 0 1 2 4
Ludovica Borsoi 0 1 2 4
John Cairns 0 1 2 4
Rosanna Tarricone 0 1 2 4
JEL Classification 0 1 2 4
0 Department of Policy Analysis and Public Management, Bocconi University , Milan , Italy
1 Department of Health Services Research, London School of Hygiene and Tropical Medicine (LSHTM) , London , UK
2 CeRGAS (Research Centre on Health and Social Care Management), Bocconi University , Via Roentgen 1, 20136 Milan , Italy
3 Michela Meregaglia
4 CCBIO (Centre for Cancer Biomarkers), University of Bergen , Bergen , Norway
Background Health-related quality of life (HRQoL) measurements from disease-specific tools cannot be directly used in economic evaluations. This study aimed to develop and validate mapping algorithms that predicted EuroQol 5-Dimensions 5-Levels (EQ-5D-5L) utilities from Functional Assessment of Anorexia-Cachexia Therapy (FAACT) and Functional Assessment of Chronic Illness TherapyFatigue (FACIT-F) and their common component (Functional Assessment of Cancer Therapy-General-FACT-G) in patients with non-small cell lung cancer cachexia. Methods Data were collected on five occasions over a 12-week period in two multicenter placebo-controlled trials. EQ-5D-5L utilities were calculated using both English and Dutch value sets. The study sample was divided into development and validation datasets according to patients' geographical residence. Generalized estimating equations were applied to five different sets of independent variables including overall, Trial Outcome Index (TOI), and individual subscales results. The best performing models were selected based on mean absolute error (MAE) and root-mean square error (RMSE). Results EQ-5D-5L and FAACT/FACIT-F results were available for 96 patients. The developed algorithms showed a good predictive performance, with acceptable MAE/ RMSE and small differences between mean observed and predicted EQ-5D-5L utilities. In FACT-G models, Physical Well-Being had the highest explanatory value, while Emotional Well-Being did not significantly affect the EQ-5D-5L score; Anorexia-Cachexia and Fatigue subscales were highly statistically significant in FAACT and FACIT-F models, respectively, as well as the TOI scores. The Eastern Cooperative Oncology Group status was included as covariate in all models. Conclusion The developed algorithms enable the estimation of EQ-5D-5L utilities from three cancer-specific instruments when preference-based HRQoL data are missing.
Mapping; FAACT; FACIT-F; FACT-G; EQ-5D-5L; Non-small cell lung cancer cachexia
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C2 · I1
Introduction
Cachexia has been defined as “a complex metabolic
syndrome associated with underlying illness and characterized
by the loss of muscle with or without the loss of fat mass”
[1]. Cachexia is a common clinical manifestation in cancer,
especially at advanced stages. The frequency of this
syndrome in lung cancer consistently outweighs that in other
malignancies [2]. Cachectic patients experience a wide range
of symptoms including lack of appetite, early satiety, and
impaired physical functioning, which cumulatively decrease
their quality of life and worsen their prognosis. Indeed,
cachexia is estimated to be the direct cause of at least 20%
of cancer deaths [3, 4].
The questionnaires belonging to the functional
assessment of chronic illness therapy (FACIT) measurement
system [5] are frequently adopted to estimate health-related
quality of life (HRQoL) in cancer studies. The functional
assessment of cancer therapy-general (FACT-G) can be used
in any tumor type and constitutes the core of all other
questionnaires addressing the specific concerns of patients with
various malignancies. Among them, the functional
assessment of anorexia/cachexia treatment (FAACT) has been
recommended in the assessment of cancer-related cachexia
[6]. The questionnaire may be administered in conjunction
with others such as the functional assessment of chronic
illness therapy-fatigue (FACIT-F), which is specifically aimed
at measuring fatigue symptoms in chronic diseases.
However, these questionnaires do not provide preference-based
scores (utilities) that are essential in the quality-adjusted life
years (QALYs) calculation for cost-effectiveness analyses.
Conversely, the EuroQol five-dimensional (EQ-5D)
questionnaire, developed by the group EuroQol [7], furnishes
preference weights from the general population to derive
QALYs and has been endorsed as a health utility standard by
the National Institute for Health and Care Excellence (NICE)
in England [8].
The measurement of HRQoL in oncology is mostly
carried out using cancer-specific instruments rather than
generic preference-based measures as they focus on relevant
health issues and tend to capture more meaningful changes
in symptomatology [9]. In a systematic literature review of
studies addressing HRQoL in cancer anorexia-cachexia
syndrome [10], FAACT was endorsed by seven studies, one of
them reporting also the common core component (FACT-G),
while only four studies adopted the generic EQ-5D
instrument. In the absence of preference-based scores, statistical
models that ”map”, or “cross-walk”, the responses from
a disease-specific instrument to utility values can be used
as an alternative solution. In recent years, there has been a
growing interest in mapping, with a number of published
algorithms predicting EQ-5D health utilities from a wide
range of disease-specific, non-preference-based scores [11].
A database maintained by the Health Economics Research
Centre (Oxford University) [12] and regularly updated with
all mapping studies published in the literature yielded 24
algorithms estimating EQ-5D scores from cancer-specific
instruments; among them, eight studies adopted a
questionnaire from the FACIT group to address a variety of tumors
(breast, prostate, colorectal, lung, and melanoma). However,
no algorithm exists specifically for anorexia-cachexia cancer
syndrome, thus making it difficult for regulatory bodies to
assess the QALY gain of new treatments for the purpose of
reimbursement in the absence of generic preference-based
data collections. The objective of this study was to develop
a reliable mapping function to estimate the 5-level EQ-5D
(EQ-5D-5L) utility values from FACT-G, FAACT, and
FACIT-F scores to inform future cost-effectiveness analyses
in the cancer cachexia setting or in lung cancer.
Methods
In performing this study, we referred to the recently
published MAPS reporting statement [13] and completed the
proposed 23-item checklist for mapping studies (Table A1,
online appendix).
Study sample
Data from two multicenter, randomized, double blind,
placebo-controlled phase 3 trials (ROMANA 1 and ROMANA
2) in patients with non-small cell lung cancer-cachexia
(NSCLC-C) were used for this analysis. The trials were
conducted at 93 sites in 19 countries between 2011 and
2014. The two studies enrolled a total of 979 patients aged
≥18 years with a diagnosis of stage III or IV NSCLC and
cachexia [defined as involuntary loss of ≥5% body weight
within 6 months or body mass index (BMI) <20 kg/m2];
patients had an estimated life expectancy of more than
4 months at enrollment and a Eastern Cooperative Oncology
Group (ECOG) performance status ≤2. Details of the
inclusion/exclusion criteria and the study design are described
elsewhere [14]. Patients were randomly assigned 2:1 to
receive active treatment (100 mg, Anamorelin HC1, Helsinn
Therapeutics, Inc.) or placebo once daily over a 12-week
period. Primary efficacy endpoints were the median change
in lean body mass and handgrip strength over the same
period. Among the secondary efficacy parameters, HRQoL
was assessed by FACIT-F and FAACT (version 4) at
baseline and weeks 3, 6, 9, and 12. Moreover, the EQ-5D-5L
questionnaire was administered at the same time points to
a subset of patients in only two sites (i.e. Poland and
Hungary). For the purpose of this analysis, we used the sample
of observations reporting both HRQoL instruments (i.e.
FACIT-F/FAACT and EQ-5D-5L) without any distinction
between the treatment arms.
Instruments
The EQ-5D-5L questionnaire is a generic, preference-based
HRQoL measure comprising five domains: mobility,
selfcare, usual activities, pain/discomfort, and
anxiety/depression [7]. Each dimension has five levels: no problems, slight
problems, moderate problems, severe problems, and extreme
problems. Patients’ responses to the questionnaire were
scored using the English EQ-5D-5L value set [15], which
ranges from −0.281 (state 55555) and 1 (state 11111,
representing perfect health) and the Dutch EQ-5D-5L value set
[16] ranging between −0.446 and 1 to illustrate the
sensitivity of results to the use of alternative value sets.
The FAACT is the anorexia-cachexia-specific HRQoL
instrument of the FACIT system [5]. It comprises the
27-item FACT-G and a 12-item Anorexia-Cachexia Subscale
(ACS). Each item is rated on a 5-point Likert scale (from 0
to 4) ranging from “not at all” to “very much”. The FAACT
is the sum of the FACT-G score (
0–108
) and the ACS score
(
0–48
) with higher values representing better health.
Similar to FAACT, the FACIT-F comprises the FACT-G and
a 13-item Fatigue Subscale (
0–52
), yielding a total score
between 0 and 160. The FACT-G is composed of four
subscales assessing physical wellbeing (PWB, 0–28), functional
wellbeing (FWB, 0–28), social/family well-being (SWB,
0–28), and emotional wellbeing (EWB, 0–24). From these
scales, it is also possible to calculate a Trial Outcome Index
(TOI), which is the sum of PWB, FWB, and tool-specific
subscales (in this study, ACS and Fatigue).
Statistical analysis
Generalized estimating equations (GEEs) were performed
in order to derive mapping functions for FAACT, FACIT-F,
and FACT-G. GEE is a technique facilitating the analysis of
data collected in longitudinal, clustered, or repeated
measures designs, which is increasingly applied in clinical trials
and biomedical studies. GEE is a population-level approach
based on a quasi-likelihood function that provides the
population-averaged estimates of the parameters. GEEs use the
generalized linear model to estimate regression parameters
allowing the specification of a working correlation matrix
that accounts for the type of within-subject correlation of
responses on the dependent variable [17, 18]. The GEE
method was selected because of the longitudinal nature of
the ROMANA trials, where repeated observations from each
instrument are expected to be correlated between visits. This
method has been used previously to predict EQ-5D utilities
from the Functional Assessment of Cancer Therapy-Prostate
(FACT-P) using data from a multicenter, randomized,
placebo-controlled trial [19]. Quasi-likelihood under the
independence model criterion (QIC) statistics were calculated in
order to select the best-working correlation structure [20].
In order to identify the best model specification, five
alternative sets of explanatory variables were compared.
Models were developed in order of increasing complexity
given by the level of disaggregation of FACIT
questionnaires scores. In detail, EQ-5D-5L scores were predicted
from the overall FACT-G, FAACT, and FACIT-F scores
(Model 1), from the generic (FACT-G) and specific (ACS
and Fatigue) components separately (Model 2), from the
four domains that compose FACT-G (PWB, FWB, SWB,
and EWB) (Model 3), from the TOI score alone (Model
4) and from the three components (PWB, FWB, and ACS
or Fatigue) of TOI scores (Model 5). Selected clinical and
demographic variables were tested for potential inclusion in
the final models on the basis of their statistical significance.
These variables were: age (>65 or ≤65), gender, body mass
index (BMI; ≤18.5 or >18.5 kg/m2), ECOG (0–1 or 2)
performance at baseline, weight loss in previous 6 months (>
or ≤10% of body weight) and current
chemotherapy/radiotherapy status (yes or no). The great majority (99.0%) of the
patients enrolled were white, thus ethnicity was not
considered as a covariate in the regression models [21]. Squared
FACIT scores were tested as well to allow for nonlinear
relationships with EQ-5D-5L utility. No imputation of missing
values was performed in order to avoid assumptions about
regarding early patient dropout. No interactions were tested,
as previous research demonstrated that adding interaction
terms seldom improved the model fit [11, 21, 22] and the
“principle of parsimony” should be embraced in developing
mapping algorithms so that they can be more readily used by
future researchers [23]. Pearson’s correlation was performed
to estimate the degree of conceptual overlap between the
source(s) and target measures that justified a mapping
exercise among them (Table A2, online appendix).
Model selection and cross‑validation
The performance of each model was assessed in terms of
how well the responses to FACT-G, FAACT, and FACIT-F
predicted EQ-5D-5L utilities. An out-of-sample validation
is usually recommended to test the algorithms; however, no
external datasets were available and an internal
cross-validation technique was applied to derive goodness-of-fit
statistics. The study sample was non-randomly divided into two
groups using one-fifth (validation sample) four-fifths
(development sample) split according to patients’ geographical
residence (i.e. Poland or Hungary). Following the approach
of a previous study [24] and recommendations from the
MAPS statement [13], we assumed that a non-random split
ensures a more efficient validation, as the two groups are
likely to differ according to some baseline characteristics.
Statistical tests (i.e. Chi-squared for categorical variables
and t test for continuous variables) were performed in order
to explore the differences between the two sub-samples.
Mapping functions were fitted on the development sample,
while the remaining observations were used to test the
models’ performance.
Model validation was performed by pooling all the
visits together in order to obtain average performance indexes
within the database. The mean absolute error (MAE) and
root-mean square error (RMSE) were calculated to
examine the differences between mean observed and predicted
EQ-5D-5L scores, with lower values indicating better
algorithm performance. The MAE is the average of absolute
differences between observed and predicted utilities, while the
RMSE is the root of the average of the squared differences.
A paired t test (p < 0.05) was also applied to the
differences between observed and mapped EQ-5D-5L scores with
significant results indicating low predictive accuracy [25].
The best performing models were selected on the basis of
the lowest MAE/RMSE results. Moreover, these differences
were compared to the minimal important difference (MID)
in EQ-5D utility that, in cancer patients, has been estimated
as 0.08 using the UK value set for the 3-level version
(EQ5D-3L) [26].
All analyses were conducted using STATA version 14.1
(College Station, TX, USA) and Microsoft Excel 2013.
Results
Descriptive statistics
Demographic and baseline clinical data stratified by
overall, development, and validation dataset are reported in
Table 1. Overall, 96 patients completed both EQ-5D-5L
and FACIT questionnaires; the number of observations per
patient ranged between 1 and 5, for a total of 420 of which
332 were used to develop the algorithms and the remaining
88 for validating them. In the overall sample (n = 96), the
majority of patients were male (68.7%) and their mean age
was 61 years; most (86.5%) were on chemotherapy or
radiotherapy treatments. The average EQ-5D-5L utility at
baseline was 0.766 (SD = 0.19), ranging between −0.102 and
1 (perfect health); mean FACT-G, FAAC,T and FACIT-F
overall scores were 64.9 (SD = 14.2), 93.2 (SD = 21.0), and
93.2 (SD = 22.4), respectively. Among FACT-G subscales,
SWB had the highest score (20.5 ± 4.7), whilst EWB had
the lowest (13.4 ± 5.0).
Patients in the development (n = 76, Poland) and
validation (n = 20, Hungary) samples differed by BMI (kg/m2),
weight loss, ECOG performance score and chemotherapy/
radiotherapy status at baseline. Significant differences
(p < 0.05) in baseline HRQoL scores were only observed in
mean EQ-5D-5L (both for English and Dutch values), FWB
and Fatigue scores. Full descriptive statistics of EQ-5D-5L,
FAACT and FACIT-F scores by visit for the overall sample
are shown in Table 2. The distribution of EQ-5D-5L scores
is shown in Fig. 1.
Regression models
respectively. Models with squared terms showed poorer
goodness-of-fit (i.e. higher QIC) compared to models
without and accordingly were not retained in the analyses (results
not shown). Based on QIC results, autoregressive correlation
was chosen within the GEE model by assuming that repeated
measures were more strongly correlated when close together
in time. The preliminary analyses testing all demographic
and clinical variables are not reported; among them, only
ECOG performance status (score = 2 vs. score = 0–1) was
included in the final models after backward selection, with
higher ECOG scores predicting lower EQ-5D-5L utility
values (negative coefficient; p < 0.001).
For all three FACIT instruments (FACT-G, FAACT, and
FACIT-F), overall scores were highly significant (p < 0.001),
as were the TOI scores, in models using aggregate results
(Model 1 and Model 4). In models using the separate
individual subscales (Model 3 and Model 5), the EWB subscale
was never significantly associated with the EQ-5D-5L score;
conversely, PWB had the highest explanatory value with
the exception of FACIT-F Model 3, where it was not
significant (p > 0.05). When ACS and Fatigue subscales were
combined with the generic FACT-G score in models 2, the
generic score was not significant in the FACIT-F model,
implying that Fatigue and ECOG were sufficient to predict
EQ-5D results. In all models, the coefficients of the HRQoL
scales had the expected (positive) signs, indicating that
better health reported by disease-specific FACT-G/FAACT/
FACIT-F tools was associated with higher EQ-5D utility.
The only exception was SWB that presented a negative
coefficient in all models, but its value was at the limits of
statistical significance.
Model selection
A synthesis of model performances across all visits in the
validation sample is reported in Table 4a, b comparing
observed and predicted EQ-5D-5L utilities using English
and Dutch preference weights, respectively. Overall, the
mapping algorithms predicted well. The absolute
differences between mean observed and mean predicted
EQ5D-5L utilities were far below the MID of 0.08 reported
for EQ-5D-3L in cancer studies [26]. Moreover, none of
the estimates fell outside the theoretical range of EQ-5D-5L
(i.e. −0.281, 1.000) with the exception of FACIT-F Model
3 (UK), but only just (i.e. 1.014). Most of the t-test
comparisons between observed and mapped scores yielded a
non-significant p value (<0.05). However, all differences
between observed and predicted values were negative, due
to an overall tendency towards over-prediction in the
poorest health states (EQ-5D-5L utility ≤0.700). At the same
time, the range of predicted EQ-5D-5L utilities was
generally narrower than the observed values and the algorithms
failed to predict the value of 1 corresponding to perfect
SD standard deviation; BMI body mass index, ECOG Eastern Cooperative Oncology Group, FACT-G functional assessment of cancer
therapygeneral, FAACT functional assessment of anorexia/cachexia treatment, FACIT-F functional assessment of chronic illness therapy-fatigue, PWB
physical wellbeing, FWB functional wellbeing, EWB emotional wellbeing, SWB social/family wellbeing, ACS Anorexia-Cachexia Subscale, TOI
trial outcome index, EQ-5D-5L EuroQol five-dimension five-level, UK United Kingdom, NL Netherlands
health especially when using FACT-G and FAACT, whilst
considerably larger intervals were obtained through
FACITF regression models.
Best performing algorithms were identified for each
FACIT questionnaire according to the lowest MAE/RMSE
scores, namely Models 3 for FACT-G and FAACT, and
SD standard deviation, FACT-G Functional assessment of cancer therapy-general, FACIT functional assessment of chronic illness therapy,
FAACT functional assessment of anorexia/cachexia treatment, FACIT-F functional assessment of chronic illness therapy-fatigue, PWB physical
wellbeing, FWB functional wellbeing, EWB emotional wellbeing, SWB social/family wellbeing, ACS Anorexia-Cachexia Subscale, TOI trial
outcome index, EQ-5D-5L EuroQol five-dimension five-level, UK United Kingdom, NL Netherlands
Model 4 for FACIT-F (both for English and Dutch values).
Scatterplots displaying observed and predicted EQ-5D-5L
utility scores for the three best performing models applied
to the validation sample (20 patients, 88 observations) are
shown in Fig. 1a, b.
Discussion
In the absence of EQ-5D or other preference-based HRQoL
measurements, mapping is a useful tool in order to estimate
utility values to be adopted in cost-effectiveness analyses.
Over the past decade, there has been a rapid increase of
mapping studies predicting generic preference-based scores from
non-preference based, disease-specific results. In the area
of oncology, eight studies have mapped EQ-5D from the
FACIT questionnaires: three studies [11, 22, 27] mapped the
general FACT-G version in various types of cancer
(including lung cancer); another three algorithms [19, 21, 28] used
the prostate-specific (FACT-P) module, while the
remaining two adopted the melanoma-specific (FACT-M) [29] and
breast-specific (FACT-B) [30] versions. Until now, no
algorithms were available to map EQ-5D scores from FAACT,
which is the FACIT tool specifically aimed at measuring
HRQoL in patients with cancer cachexia; nor is there a
mapping algorithm for the FACIT-F. Moreover, the existing
algorithms map to the EQ-5D-3L rather than the EQ-5D-5L;
thus, it was not possible to apply these functions to our
database of EQ-5D-5L utilities. In addition, most mapping
studies have used cross-sectional data and Ordinary Least Square
(OLS) regression in modeling FACIT scores; even when
data from multiple time points were available, only baseline
information were used [21] or repeated observations were
pooled together in order to increase the sample size [31].
This study, following the approach of a previous one
[19], estimated GEE models to account for the longitudinal
nature of the data. To increase the usability of the mapping
algorithms, common demographics (e.g. age) and clinical
variables (e.g. BMI), which are likely to be collected in
clinical studies dealing with cancer cachexia or NSCLC were
initially tested in the models. Among them, only ECOG
performance score displayed a significant coefficient (p < 0.05)
and was retained in the final models. Moreover, in addition
to developing a mapping function to predict EQ-5D-5L
utilities from FAACT and FACIT-F, we provided separate
algorithms for the general instrument (FACT-G). Disaggregated
models predicting EQ-5D-5L utility values from individual
FACIT subscales were found to have better predictive ability
in the case of FACT-G and FAACT, while the best
performing algorithm for FACIT-F was that modeling the TOI score.
These results are aligned with the current mapping literature
which has shown greater explanatory power from
regression models using disaggregated information instead of
summary scores from a disease-specific measure [22].
Nevertheless, all the algorithms performed quite well; overall,
MAE and RMSE values were comparable across the models
and lower than those reported by other mapping studies [19,
24, 32]. No considerable differences were found between
the algorithms developed using the English and the Dutch
value sets, which identified the same best performing models
(Model 3, Model 3, Model 4) within each FACIT
instrument; however, mapping using English weights performed
slightly better in terms of lower MAE/RMSE and smaller
differences between mean observed and mean predicted
EQ5D-5L utilities. The estimated coefficients are aligned with
those reported by a previous study [11] mapping
FACTG in cancer patients affected by breast, colorectal or lung
cancer, where regression coefficients for the overall score
were between 0.005 and 0.008 (according to the technique
adopted) and coefficients for individual subscales fell in
the interval 0.006–0.013 for PWB, 0.005–0.010 for FWB,
0.002–0.008 for EWB. Our slightly lower estimates may be
a consequence of the different regression method (GEEs)
applied, or possibly is reflecting differences in the patient
groups, for example, in terms of severity.
This study presents a few limitations. First, in
calculating EQ-5D-5L utilities, we adopted country preference
weights that may not be the best estimates for Hungarian
and Polish populations. However, due to unavailability of
EQ-5D-5L tariffs for these two countries, we selected the
ECOG is a dummy variable assuming the value of 1 for ECOG = 2 and 0 otherwise (ECOG = 0.1)
Model 1 algorithm using FACIT overall score(s), Model 2 algorithm using general (FACT-G) and questionnaire-specific scale(s) separately,
Model 3 algorithm using the four FACT-G wellbeing subscales, Model 4 algorithm using FACIT TOI score(s), Model 5 algorithm using TOI
subscales separately, GEE generalized estimating equations, UK United Kingdom, NL Netherlands, SE standard error, FACT-G functional
assessment of cancer therapy-general, FAACT functional assessment of anorexia/cachexia treatment, FACIT-F functional assessment of chronic
illness therapy-fatigue, PWB physical wellbeing, FWB functional wellbeing, EWB emotional wellbeing, SWB social/family wellbeing, ACS
Anorexia-Cachexia Subscale, TOI trial outcome index, ECOG Eastern Cooperative Oncology Group
*p value <0.05
**p value <0.01
***p value <0.001
English value set (mappings to the EQ-5D-3L have most
commonly used this set) and the Dutch one, which are the
two EQ-5D-5L sets of weights closest to the ROMANA
trials populations [7].
Second, model validation used a non-random split-sample
method, rather than testing the algorithms in an external
dataset that would be the preferred approach according to
the MAPS Statement [13]. However, significant differences
were found in relevant baseline patients characteristics (i.e.
BMI, weight loss, ECOG, chemotherapy/radiotherapy
status) between the two samples, thus the cross-sample
validation was likely to have been conducted on a quasi-different
NSCLC-C population. A completely different approach has
been suggested by most recent guidelines [33], which
recommend not splitting the sample for validation purposes if
this implies a further reduction of a (small) sample size.
Third, the mapping functions were developed using
a small database, since only 96 patients enrolled in the
ROMANA trials were invited to complete the EQ-5D-5L.
However, because the patients completed the HRQoL
questionnaires on up to five occasions, 420 observations were
available for analysis, 79% (n = 332) of which were used to
obtain regression coefficients and 21% (n = 88) to validate
the mapping algorithms.
Fourth, the sample of NSCLC-C patients who were likely
to have a better health status (i.e. ECOG ≤2, life expectancy
at least of 4 months) than the general population with the
same medical condition due to the ROMANA trials’
inclusion criteria. Thus, the generalizability of the developed
mapping functions to other NSCLC-C samples should
consider any potential clinical differences.
Finally, as already observed in the literature [11, 34], all
mapping algorithms tend to over-predict utility values for
patients in poor health and, conversely, under-predict the
highest scores. Moreover, they systematically fail to predict
the value of 1 corresponding to perfect health, although in
our database, due to the severity of the NSCLC-C
condition, less than 20% of EQ-5D-5L observations were at the
ceiling at each study visit, which is lower than observed
in another mapping study using the same tool [30]. This
bias affected the FACIT-F algorithms less than FACT-G
and FAACT ones and, within the same FACIT instrument,
models using disaggregated scales (Model 3) instead of
summary scores (Model 1). Skaltsa et al. [19] developed
groupspecific models according to disease severity to increase
prediction accuracy at the “extremes”; unfortunately, due to
the small database, this approach was infeasible in this study.
As population-average models, the estimated GEE functions
UK United Kingdom, NL Netherlands, SD standard deviation, FACT-G functional assessment of cancer therapy-general, FAACT functional
assessment of anorexia/cachexia treatment, FACIT-F functional assessment of chronic illness therapy-fatigue, MAE mean absolute error, RMSE
root mean squared error
aT test
performed well in predicting mean utility values, which are
usually required to populate model-based economic
evaluations. New techniques such as beta-binomial regression and
limited dependent variable mixture (LDVM) models [22, 23,
35], which better fit the typical distributions of EQ-5D data,
are emerging in the mapping literature to overcome the
wellknown limitations of linear models and might be explored in
future research with larger databases.
Conclusion
Given the increasing costs of cancer care it is important
to support the health-related decision-making process
of allocating scarce resources by assessing the value of
treatments through economic evaluation techniques such
as cost-utility analysis [36]. Previous research showed that
cancer cachexia, mainly affecting lung cancer patients, has
RMSE
–
a significant impact on patients HRQoL and healthcare
resources utilization [10]. This study provided algorithms
to predict EQ-5D-5L utility values from FACT-G, FAACT
and FACIT-F scores, confirming that mapping may
represent a useful tool in the absence of preference-based
HRQoL scores. These algorithms could be applied in other
studies related to cancer cachexia or NSCLC in general,
by those requiring EQ-5D-5L utility values for QALY
calculation.
Acknowledgements
Helsinn Therapeutics.
This study was supported by a grant from
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(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
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