01112012  Uitgave 9/2012 Open Access
Comparing higher order models for the EORTC QLQC30
 Tijdschrift:
 Quality of Life Research > Uitgave 9/2012
Belangrijke opmerkingen
Disclaimer: The contents of this publication and methods used are solely the responsibility of the authors and do not necessarily represent the official views of the EORTC.
Abbreviations
AIC
Akaike Information Criterion
AP
Appetite loss
CFI/TLI
Comparative Fit Index/Tucker–Lewis Index
CF
Cognitive function
CO
Constipation
DF
Degrees of freedom
DI
Diarrhea
DY
Dyspnea
EF
Emotional function
EORTC
European Organization for Research and Treatment of Cancer
FA
Fatigue
HRQoL
Healthrelated quality of life
MIMIC
Multiple indicator, multiple cause
NV
Nausea and vomiting
PA
Pain
PF
Physical function
PROMIS
Patientreported outcomes measurement information system
QLQC30
Quality of Life Questionnaire core 30 items
RF
Role function
RMSEA
Root mean square error of approximation
SL
Insomnia
SF
Social function
WHO
World Health Organization
WLSMV
Weighted least squares estimator with adjustment for means and variance
Introduction
Since its release in 1993, the EORTC QLQC30 has become a widely used “core” instrument for the study of cancerspecific healthrelated quality of life (HRQoL) [1–4]. It comprises 9 multiitem scales and 6 singleitem measures. While the multidimensional profile generated by the QLQC30 is invaluable in providing a detailed picture of the impact of cancer and its treatment on patients’ HRQoL, there is also interest in developing “summary” scores that can simplify analyses and minimize the chance of Type I errors due to multiple comparisons. In addition, it might sometimes be more useful, particularly in clinical trials, to employ a composite variable measured with greater precision [5], as opposed to many variables, each measured with less precision. This interest in summarizing data generated from multidimensional HRQoL profiles is reflected in the development of socalled “higher order models,” such as those available for the SF36 Health Survey and other instruments [6–8].
To date, there have been a limited number of analyses of the structure of the QLQC30, all of which relied on either relatively small sample sizes (e.g., N < 200), a subset of the QLQC30 items, and/or exploratory techniques [9–15]. The aim of the present study was to fill this gap, by examining empirically and comparing the statistical “fit” of a number of alternative “higher order” measurement models for the QLQC30, using confirmatory factor analysis in a large sample of patients [16]. The results of this study may be used to identify one or more, higher order measurement models that could be used for the computation of simpler, summary scores for this questionnaire. The results are also of interest from a theoretical perspective, hopefully allowing us to place the pragmatically oriented QLQC30 in the context of a number of established, theoretical HRQoL models.
Methods
Data source
The data used in this study were originally collated for the CrossCultural Assessment Project of the EORTC Quality of Life Group, and have been described elsewhere [17, 18]. Briefly, a total of 124 individual datasets were received: 54 from the EORTC Data Center, with permission from the relevant EORTC Clinical Groups, and an additional 70 datasets from other individuals and organizations from around the world. Included were datasets from 48 countries and for 33 translations of the QLQC30. The resulting dataset consisted of 38,000 respondents, of whom more than 30,000 completed baseline (pretreatment) questionnaires. Of these 30,000 respondents, 9,044 completed the most recent version (3.0) of the QLQC30. We selected a 50% random sample for the present investigation. The remaining observations were retained for future analyses.
Relevant information from each dataset was extracted, recoded into a standard format, and combined into one large project database. In addition to the QLQC30, other data collected included age, gender, country, language of administration, primary disease site, and stage of disease.
The QLQC30
The EORTC QLQC30 version 3.0 [1–4] includes 30 items comprising 5 multiitem functional scales (physical (PF), role (RF), cognitive (CF), emotional (EF), and social (SF)), 3 multiitem symptom scales (fatigue (FA), nausea and vomiting (NV), and pain (PA)), 6 singleitem symptom scales (dyspnea (DY), insomnia (SL), appetite loss (AP), constipation (CO), diarrhea (DI), and financial difficulties (FI)), and a twoitem global quality of life scale (QL). The FI item was excluded from all of the present analyses, as it may be considered peripheral to the other scales in the instrument, and often is left unreported in the literature The questionnaire uses a 1week time frame and 4point Likerttype response scales (“not at all,” “a little,” “quite a bit,” and “very much”), with the exception of the two items of the overall QL scale which use a 7point response scale.
The QLQC30 has been shown to be reliable and valid in a range of patient populations and treatment settings. Across a number of studies, internal consistency estimates (Cronbach’s coefficient α) for the scores of the multiitem scales exceeded 0.70 [3]. Test–retest reliability coefficients range between 0.80 and 0.90 for most multiitem scales and single items [19]. Tests of validity have shown the QLQC30 to be responsive to meaningful betweengroup differences (e.g., local vs. metastatic disease, active treatment vs. followup) and changes in clinical status over time [1, 3].
Measurement models
Seven HRQoL measurement models [20–22] were fit to the data. The models were chosen on the basis of a review of recent HRQoL literature, general knowledge of psychometric literature, discussions among the coauthors, and suggestions made by external experts. Analyses were conducted by means of confirmatory factor analysis. The fit of each model was considered separately, and in relationship to the other models when possible.
The models to be compared in this study were organized in 3 branches of nested models, each branch beginning with the same Standard model in the root node. The first branch consists of the Standard model, followed by a twodimensional Physical health–Mental health model, a twodimensional Physical burden–Mental function model, and culminating in a onedimensional HRQL model. The second branch begins with the Standard model, followed by a twodimensional Burden and Function model, and again culminating in the—same—onedimensional HRQL model just mentioned. Finally, the third group of models utilizes a different, socalled “formative”—or “causal”—approach to measurement. Two variants, a fixed weight and a free weight, of these formative models are included in this branch. These two models are nested within a third “branch” emanating from the Standard model mentioned above.
These 7 models are described in more detail below. (See Fig. 1 for a graphical representation of the models. (Straight lines, with onesided arrows, represent regression coefficients; arced lines, with twosided arrows, represent correlation coefficients.)
(1)
The Standard 14dimensional QLQC30 model corresponding to the original 13 QLQC30 scales and one overall QL scale, with each scale modeled as a firstorder latent variable. All firstorder factors were allowed to correlate with each other. Here we also assumed that the singleitem symptom scales were manifestations of latent variables (the socalled “spurious” model [23]). This Standard model formed a fundamental “building block,” used as the basis for all of the other models described here.
(2)
The twodimensional, Physical health and Mental health model, which has been used for the SF36 [6, 7], has been considered in a large, multiinstrument study [24] and is consistent with the PROMIS domain mapping project and the WHO framework [25–27]. Unfortunately, it is difficult to map the QLQC30 a priori to the physicalmental distinction in only one, unambiguous manner (see, e.g., [24] for an alternative mapping). In the current case, implementation of the Physical–Mental model requires that some symptomrelated firstorder latent variables map to the Mental as well as the Physical higher order factors. Specifically, PF, NV, DY, AP, CO, and DI were allowed to load only on the Physical higher order factor; EF and CF were allowed to load only on the Mental factor; while RF and SF, and the symptoms FA, PA, and SL were allowed to load on both the Mental health and Physical health factors. We assumed that QL was not subsumed by either higher order component.
(3)
This variant of the previous model, labeled the Physical burden and Mental function model, requires all symptom firstorder latent variables to load onto only one higher order factor. Thus, PF, FA, NV, PA, DY, SL, AP, CO, and DI were allowed to load only on the Physical burden factor; EF and CF were allowed to load only on the Mental function factor; and RF and SF were allowed to load on both factors. Again, QL was not subsumed by either higher order component.
(4)
The Wilson and Cleary model [28] describes HRQoL as consisting of (a sequence of causal effects between) 5 groups of latent variables: physiological states, symptom status, functional status, general health perception, and overall HRQoL. This model was recently tested in a structural equation model [29], using a number of different instruments in a sample of HIV/AIDS patients. This model also seems to have a natural correspondence with the content of the QLQC30, which emphasizes symptom burden, functional health, and overall QoL. Thus, paralleling this approach, PF, SF, RF, CF, and EF were only allowed to load on Function; and FA, NV, PA, DY, SL, AP, CO, and DI were only allowed to load on Burden. Again, QL was not subsumed by either higher order component.
(5)
The parsimonious, and highly restrictive, onedimensional HRQL model has recently been considered using the QLQC30 in a multicultural sample of cancer patients [13, 14]. It assumes that all firstorder latent variables (with the exception of QL) load on only one underlying HRQL dimension. Again, QL remained unsubsumed.
(6) & (7)
Boehmer and Luszczynska [9] published a study of a model inspired by the work of Fayers et al. (e.g., [30, 31],). It is somewhat similar to the BurdenFunction model presented above, yet allows the symptom items to simultaneously play the role of reflective indicators for Burden (or “symptomatology”) and formative indicators for Function. This model illustrates the potentially important distinction between formative and reflective scales, and the ongoing controversy concerning their use and interpretability [23, 32–36]. Formative scales, when misspecified as being reflective, will generally lead to bias and poorer model fit [37]. We therefore include a formative variant of Burden, to be used in the BurdenFunction model mentioned above. As formative scales can have either equal, fixed weights, or freely estimated weights for their components, we consider both types of weighting, forming models (6) and (7). This model architecture is also closely related to the “multiple indicator, multiple cause” (MIMIC) model [38].
×
Statistical analysis
The 7 models described above were fitted to the QLQC30 version 3.0 item scores. All of these models were fitted under the following assumptions and methods:
Basic model architecture
The original QLQC30 multi and singleitem measures were modeled as firstorder latent variables. The QL scale was also included in the models as a latent variable, and was allowed to covary with all other (higher order) latent variables, yet remained distinct from other higher order latent variables. Only those items originally associated with a specific scale were associated with the corresponding latent variable. All items were treated as being ordinal.
In order to identify latent variable models, it is customary to fix one of the item loadings to a value of 1.0. (Both loadings of items corresponding to the QL latent variable were also fixed.) This problem of model identification is especially critical for latent variables having only one item/indicator, and requires one to also fix the error variances for the five latent variables with only a single indicator. We therefore estimated the reliability of the one item latent variables on the basis of test–retest correlations reported elsewhere [19], and accordingly fixed the latent error variances to be equal, at 20% of the total variance for these latent variables [39]. This assumption is tantamount to assuming that the singleitem scales perform satisfactorily, even though they are not perfect. Preliminary analyses indicated that modelfit statistics were only slightly affected by varying this assumption within reasonable bounds. This architecture corresponds to the Standard model mentioned above. It may also be viewed as a liberalization of the original QLQC30 scales, for it allows unequal item weights, assumes an ordinal measurement level for each item, and estimates error variances where possible.
Estimators
As all items were treated as being ordinal, polychoric correlations were estimated and a (robust) weighted least squares estimator with adjustment for means and variance (WLSMV)—with MPLUS’ default “delta” parameterization—was used [40]. This estimator is robust for small sample sizes and deviations from normality [41] and is nearly optimal for multilevel models [42]. The WLSMV estimator utilizes pairwise deletion of missing observations as default. Alternative, (robust) maximum likelihood estimators would have required numerical integration—or Monte Carlo simulations—in more than 14 dimensions, which would present a computational burden straining the capacity of modern, desktop computers.
Tests of model fit and other fit indices
The χ^{2} test of model fit was examined. The χ^{2} test is sensitive to sample size, leading easily to rejection of the null hypothesis in models with a large number of observations. Approximate goodnessoffit indices (AGFI) are less sensitive to sample size: the CFI/TLI (Comparative Fit Index/Tucker–Lewis Index) and the RMSEA (Root Mean Square Error of Approximation). There is a great deal of controversy concerning the proper use of the chisquare and AGFI (e.g., [43–48]), and since we foresee no consensus on this matter in the near future, we will report both [49, 50]. A commonly used rule of thumb is that a RMSEA < 0.05 indicates close approximate fit, while values between 0.05 and 0.08 indicate acceptable fit, and values >0.10 indicate poor approximate fit [51]. Another rule of thumb is that a value of CFI or TLI > 0.95 indicates good fit and a value > 0.90 indicates acceptable fit [50]. Differences ≥ 0.01 between (pairs of) TLIs/CFIs and RMSEAs are considered to be substantial enough to merit attention [52]. In the case of inadequate model fit, modification indices and residuals were examined, in order to detect possible causes.
Direct comparisons of models by computing the differences between their respective chisquares are not appropriate when using WLSMV estimators, and requires some additional computations [53, 54]. Direct comparisons between model chisquares can only be made when one model is nested within the other model.
Correction for cluster sampling
The dataset was composed of data collected from dozens of different studies of various populations. It was suspected that this heterogeneity in populations and procedures could lead to biased parameter estimates and fit statistics. For this reason, a correction was made to the estimation procedure to take cluster sampling into account, and to adjust the standard errors and chisquare statistics [42, 55, 56]. Additional techniques, such as utilizing sampling weights [57, 58], other (i.e., maximum likelihood) estimators, or attempting to explicitly model the sampling process, may also have added value, but were not utilized in the current analysis. In the present case, a cluster was defined as a dataset from a source with a unique study identifier code, possibly extended with the treatment group as coded in the original dataset.
Software
Analyses were conducted using the Mplus v.5.2 program [59].
Statistical significance
Unless otherwise indicated, a significant result is defined as P < 0.01.
Results
The characteristics of the patients included in the study are presented in Table 1. The average age of the patients was 60 years, with slightly more males than females, and more early than advanced cancer. A number of study types (clinical trials, nonrandomized comparative studies, and observational studies), a wide variety of (primarily European) countries, and a range of disease sites were also represented.
Table 1
Respondent characteristics (N = 4,541)
Mean (SD)  % Missing  

Age  59.6 (12.6)  9.9 
N
 %  

Gender  
Male  2,511  55.3 
Female  1,906  42.0 
Unknown  124  2.7 
Stage  
I–III  1,846  40.7 
IV–recurrent/metastatic  1,765  38.9 
Unknown  930  20.5 
Site  
Breast  663  14.6 
Colorectal  245  5.4 
Gynecological  375  8.3 
Head and neck  801  17.6 
Lung  610  13.4 
Esophagus/stomach  822  18.1 
Prostate  405  8.9 
Other  620  13.7 
Study type  
RCT  1,561  34.4 
NonRCT  1,455  32.0 
Field study  1,386  30.5 
Unknown  139  3.1 
Country  
Belgium  193  4.3 
Canada  120  2.6 
France  266  5.9 
Germany  477  10.5 
Netherlands  228  5.0 
Norway  498  11.0 
Spain  402  8.9 
Sri Lanka  438  9.6 
Sweden  202  4.4 
UK  722  15.9 
USA  157  3.5 
Other  838  18.5 
No item had more than 2.6% missing observations; for most items this was less than 1%. However, all items, with the exception of the two items of the QL scale, were highly skewed; approximately half of the items had 50% or more of the responses in the lowest category (data not shown). The polychoric correlations between the 29 items were generally moderate (i.e., >0.30) to strong (>0.50) (data not shown).
The fit indices for the various models are presented in Table 2. As might be anticipated given the large sample size, no model passed the stringent χ^{2} test of model fit. However, all models were deemed to be at least “adequate” approximations to the data, as determined by the previously noted rules of thumb applied to the CFI/TLI and RMSEA indices. As expected [20], the less restricted the model, the better the model fit, with the Standard model even achieving a “good” fit. The Mental–Physical models had approximate fit indices slightly superior to all of the other higher order models. The correlations between higher order factors (in the multifactor models) were generally quite high, often exceeding 0.95 (see Table 2). This indicates that these higher order factors were virtually indistinguishable, thus implying that additional factors were of limited explanatory value. Exceptions are the models positing Mental and Physical factors, which have lower correlations between these higher order factors.
Table 2
Tests^{a} and approximate goodnessoffit indices for various models
Model  χ^{2}* 
df
 CFI/TLI  RMSEA  Remarks 

1. “Standard” model  134  15  0.96/0.98  0.042  14 Latent variables, excluding FI 
2. Physical health, mental health and QL  234  19  0.92/0.98  0.050  Correlation physical health and mental health = 0.74 
3. Physical burden, mental function and QL  248  18  0.92/0.97  0.053  Correlation physical burden and mental function = 0.81 
4. Symptom burden, function and QL  294  18  0.90/0.97  0.058  Correlation burden and function = 0.97 
5. HRQL and QL  297  18  0.90/0.97  0.058  
6. Formative symptom burden (free weights), function and QL  277  17  0.91/0.97  0.058  Correlation formative burden and function = 0.96 
7. Formative symptom burden (fixed weights), function and QL  300  17  0.90/0.96  0.061  Correlation formative burden and function = 0.95 
The results of (corrected) chisquared difference tests between pairs of models within each branch of nested models [53] are presented in Table 3. Differences between each successive pair of nested models in each branch were significant, indicating that each successive tightening of restrictions resulted in a significant decrement in model fit.
Table 3
χ^{2} Difference testing between 3 branches of nested models
Model  Δχ^{2} wrt previous model in Branch 1 
df
 Δχ^{2} wrt previous model in Branch 2 
df
 Δχ^{2} wrt previous model in Branch 3 
df


1. Standard model (14 latents), incl. QL  Root node  Root node  Root node  
2. Physical health, mental health and QL  293  17  –  –  –  – 
3. Physical burden, mental function and QL  77  2  –  –  –  – 
4. Symptom burden, function and QL  –  –  377  15  –  – 
5. HRQL and QL  241  3  47  2  –  – 
6. Formative symptom burden (free weights), function, and QL  –  –  –  –  336  12 
7. Formative symptom burden (fixed weights), function, and QL  –  –  –  –  241  5 
The standardized regression weights (for the firstorder factors on the higher order factors) for the best fitting models for each of the three branches are presented in Table 4. The percentage of variance for each firstorder factor explained by their corresponding higher order factor is presented as well. All postulated factor regression weights for the Burden/Function and the Mental health/Physical health model were significant, with the exception of SL on the Physical health factor. However, the percentages of explained variance for PF, EF, CF, and SL are markedly inferior for the Burden/Function model.
Table 4
(Standardized) Regression weights for firstorder factors and percentage variance explained by best fitting higher order model for each of three branches of (nested) models
Firstorder factors  Physical/mental health (model # 2)  Burden/function (model #4)  (free wgt.) Formative burden/function (model #6)  

Physical  Mental 
R
^{2}
 Burden  Function 
R
^{2}
 (free) Formative burden  Function 
R
^{2}
 
PF  0.80^{a}
 0.64  0.76*  0.58  0.76^{a}
 0.59  
RF  0.89*  0.04  0.84  0.89^{a}
 0.79  0.89*  0.80  
EF  0.72^{a}
 0.52  0.62*  0.38  0.62*  0.38  
CF  0.90*  0.82  0.80*  0.63  0.80*  0.62  
SF  0.42*  0.46*  0.68  0.82*  0.67  0.82*  0.67  
FA  0.82*  0.19*  0.93  0.97^{a}
 0.95  0.83^{a}
 NA  
NV  0.66*  0.43  0.65*  0.42  0.04  NA  
PA  0.60*  0.23*  0.62  0.79*  0.63  0.16*  NA  
DY  0.80*  0.65  0.80*  0.64  0.03  NA  
SL  0.05  0.77*  0.64  0.77*  0.59  0.08*  NA  
AP  0.85*  0.72  0.84*  0.71  −0.08  NA  
CO  0.75*  0.56  0.73*  0.54  0.04  NA  
DI  0.62*  0.39  0.62*  0.38  −0.02  NA 
Only the hypothesized regression weights for the FA, SL, and PA symptom scales for the formative Burden/Function model (in the third branch of nested models) were statistically significant. FA was the only symptom with a substantial loading on the formative Burden variable, which more or less ignored the other symptoms. The amount of explained variance was again inferior for the PF, SF, and CF scales, as compared to the Mental health/Physical health model.
Examination of the modification indices and residuals indicated that item q22 (“worry”) was a source of illfit for all models. There also appeared to be some relationships between EF and the other scales not fully captured by the higher order factors (data not shown).
Discussion and conclusions
The present study tested the statistical fit of seven alternative measurement models for the QLQC30. This was done by using confirmatory factor analysis to compare empirically their adequacy in representing the EORTC QLQC30 in a sample of 4,541 cancer patients. The point of reference was the Standard model, a latent variable model which employed the architecture of the standard, 14dimensional QLQC30 model (excluding the FI item).
As mentioned previously, the models studied here were organized into three independent branches of nested models: three models in the socalled Mental–Physical branch, two in the BurdenFunction branch, and two in the “formative” BurdenFunction branch. The Standard model stands at the apex of each of the three branches.
None of the models examined passed the stringent χ^{2} test of model fit, indicating that none of these models captured all of the systematic variation in the data. It should be noted, however, that with 4541 observations, a chisquare test is quite sensitive to detecting small deviations. Importantly, all models demonstrated at least an “adequate” approximation to the data [50]. The Standard QLQC30 model actually demonstrated a “good” fit to the data. Moreover, χ^{2} “difference testing” demonstrated that each addition of restrictions in each of the successively nested models in each branch led to a statistically significant deterioration in model fit.
The MentalHealth/ PhysicalHealth model, the least restricted higher order model in the first branch studied, is significantly better than its nested alternatives, and gives an adequate, albeit imperfect, approximation to the data. The Burden/Function model was the best approximation to the Standard model in the second branch. We note that the Burden/Function model is only slightly superior to the simpler onedimensional HRQL model, for its two dimensions are almost indistinguishable.
Unfortunately, we cannot use the chisquare test to directly test the fit of the models nested in these two, different branches. However, we did use the approximate fit indices to compare those models, with the results indicating that the Mental Health/PhysicalHealth model is slightly superior to the Burden/Function model. Additionally, the MentalHealth/PhysicalHealth model achieves better explanatory power for the CF, PF, EF, and SL scales than does the Burden/Function model. For these reasons, the MentalHealth/PhysicalHealth model is preferable.
A third branch of nested models, consisting of “causal” or “formative” latent variables, represents an alternative approach for the modeling of HRQL questionnaires. The model with free weights was a statistically better fit to the data than the fixed (equally weighted) model. However, the potential improvements in fit indices, which are to be expected if the formative conceptualization was more appropriate than the reflective one [37], were not observed in the current analysis. Additionally, the only symptom that appears to strongly predict Function is fatigue, a result also reported previously [9]. This indicates that the other symptoms may be regarded as largely irrelevant as predictors of Function for this group of patients, which may be an overly zealous simplification of the Standard model. One could argue that this result disqualifies this branch of models.
It is interesting to note that question 22 (i.e., “did you worry?”) of the QLQC30 emotional function scale was frequently flagged as being a source of illfit. This may have to do with possible ambiguity in the meaning of “worry,” either as an indication of healthy concern in a difficult situation, or as an indication of psychological distress.
Several possible limitations of this study should be noted. First, the use of pairwise deletion for (the relatively sparse) missing data in the computation of the polychoric correlations resulted in some loss of data. A second limitation concerns the possible bias introduced from the clustered sampling of data from various data sources. While we did apply a correction to the chisquare statistics and standard errors, additional corrections for parameter estimates, possibly based on sampling weights, would arguably have been even better. Third, it would have been useful to have access to Akaike Information Criterion (AIC), and other related statistics [60] in order to compare nonnested models across the various branches. The use of full information maximum likelihood estimation procedure could have provided a solution for all three problems simultaneously; however, the computational burden for such an estimation procedure is prohibitive.
A fourth limitation concerns the choice of models, which was neither exhaustive for all plausible, theoretical models, nor sufficient for capturing all of the systematic variation in the data. On the other hand, the “alternative models” approach used here is methodologically stronger than a purely exploratory approach [16]. For this reason, we refrained from “tweaking” either the standard or any of the other alternative models in order to achieve some improvement in fit, a practice frowned upon as potentially capitalizing on chance. Nevertheless, we recognize that there are other, more exploratory approaches that might be used. For example, causal discovery techniques and software (e.g., TETRAD) employ rigorous algorithms to locate all wellfitting models for a set of observed data, to which theory can then be applied to choose the most suitable or plausible model(s). While beyond the scope of the current paper, the utility of such approaches could be the subject of future studies [61, 62].
Summarizing, we believe that the PhysicalHealth/MentalHealth model is the most appropriate conceptualization for our goal of offering a simplified form of QLQC30 outcomes. This model was found to provide an “adequate” fit to the data, slightly superior to the alternative, higher order models examined here. We believe that it is the best of the approximations to the Standard model considered in this study. The Physical Health/MentalHealth conceptual model has also been utilized and successfully tested for other HRQoL instruments [6, 7], has been considered in a large, multiinstrument study [24], and is consistent with the PROMIS domain mapping project and the WHO framework [25–27]. For these reasons, we consider it to be the most promising of the models considered here.
Nevertheless, the “superiority” of this PhysicalHealth/MentalHealth model is modest, and it remains to be seen whether its extra complexity—as compared to e.g., the simple HRQL model—provides tangible (clinical) benefits. We therefore intend to further examine the suitability of the PhysicalHealth/MentalHealth model by testing its measurement equivalence across subpopulations and over time. We will also attempt to use this model to predict external criteria and outcomes, as well as comparing it to other instruments purporting to measure similar concepts. These efforts will culminate in an algorithm for the computation of higher order factors for the QLQC30.
Acknowledgments
We gratefully acknowledge the many individuals who contributed datasets to this study. This work was funded by the EORTC Quality of Life Group and the Netherlands Cancer Institute, and carried out under the auspices of the EORTC Quality of Life Group. The authors thank the EORTC Headquarters and the various EORTC Clinical Cooperative Groups for permission to use the data from their trials for this research. Some of the results of this study were presented at the Annual Conference of the International Society for Quality of Life Research, New Orleans, USA, October 30, 2009.
Open Access
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Open AccessThis is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/bync/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Appendix
Members of the EORTC Quality of Life Group CrossCultural MetaAnalysis Project
Australia: M. King, S. Leutenegger, N. Spry; Austria: E. Greimel, B. Holzner; Belgium: A. Bottomley, C. Coens, K. West; Brazil: G. de Castro, C. de Souza; Canada: A. Bezjak, M. Whitehead; Denmark: M. Groenvold, M. Klee, M. Petersen; France: A. Bredart, T. Conroy, C. Rodary; Germany: M. Berend, B. Bestmann, M. Koller, O. Krauß, T. Kuchler, B. Malchow, R. Schwarz; Greece: K. Mystakidou; Iran: A. Montazeri; Italy: C. Brunelli, M. Tamburini; Japan: T. Matsuoka, H. Zhao; Netherlands: N. Aaronson, A. de Graeff, C. Gundy, R. de Leeuw, M. Muller, M. Sprangers; Norway: K. Bjordal, E. Brenne, M. Hjermstad, M. Jordhøy, P. Klepstad, S. Sundstrøm, F. Wisløff; Singapore: Y. B. Cheung, S.B. Tan, J. Thumboo, H. B.Wong; South Korea: Y. H. Yun; Spain: J. Arraras; Sri Lanka: H. Jayasekara, L. Rajapakse; Sweden: M. AhlnerElmqvist; Switzerland: P. Ballabeni, J. Bernhard; Taiwan:W.C. Chie; Turkey: U. Abacioglu; UK: J. Blazeby, J. Bruce, A. Davies, P. Fayers, L. Friend, Z. Krukowski, T. Massett, J. Nicklin, J. Ramage, N. Scott, A. SmythCull, T. Young; USA: D. Cella, D.L. Esseltine, C. Gotay, I. Pagano.
Contributing groups
European Organization for Research and Treatment of Cancer (EORTC) Brain Cancer Group, EORTC Breast Cancer Group, EORTC Chronotherapy Group, EORTC GastroIntestinal Group, EORTC GenitoUrinary Group, EORTC Gynecological Group, EORTC Head and Neck Cancer Group, EORTC Leukemia Group, EORTC Lung Cancer Group, EORTC Lymphoma Group, EORTC Melanoma Group, EORTC Quality of Life Group, EORTC Radiotherapy Group, EORTC Soft Tissue Group, National Cancer Institute Grant CA60068, National Cancer Institute of Canada (NCIC) Clinical Trials Group, Swiss Group for Clinical Cancer Research (SAKK).