- Open Access
Development of an item bank for the EORTC Role Functioning Computer Adaptive Test (EORTC RF-CAT)
© Gamper et al. 2016
Received: 9 June 2015
Accepted: 28 April 2016
Published: 6 May 2016
Role functioning (RF) as a core construct of health-related quality of life (HRQOL) comprises aspects of occupational and social roles relevant for patients in all treatment phases as well as for survivors. The objective of the current study was to improve its assessment by developing a computer-adaptive test (CAT) for RF. This was part of a larger project whose objective is to develop a CAT version of the EORTC QLQ-C30 which is one of the most widely used HRQOL instruments in oncology.
In accordance with EORTC guidelines, the development of the RF-CAT comprised four phases. Phase I involved the conceptualization of RF. In Phase II, a provisional list of items was defined and revised by experts in the field. In phase III, feedback was obtained from cancer patients in various countries. Phase IV comprised field testing in an international sample, calibration of the item bank, and evaluation of the psychometric performance of the RF-CAT.
Phases I-III yielded a list of 12 items eligible for phase IV field-testing. The field-testing sample included 1,023 patients from Austria, Denmark, Italy, and the UK. Psychometric evaluation and item response theory analyses yielded 10 items with good psychometric properties. The resulting item bank exhibits excellent reliability (mean reliability = 0.85, median = 0.95). Using the RF-CAT may allow sample size savings from 11 % up to 50 % compared to using the QLQ-C30 RF scale.
The RF-CAT item bank improves the precision and efficiency with which RF can be assessed, promoting its integration into oncology research and clinical practice.
In health-related quality of life (HRQOL) research, there is increasing interest in generating computer-adaptive test (CAT) versions of HRQOL measures . CAT facilitates greater measurement precision and may reduce test length by tailoring the set of questions asked to the (estimated) level of functioning or symptom burden of each patient. This makes CAT attractive for research as the increased measurement precision usually entails reductions in required sample sizes making HRQOL data collection more feasible. In addition, the use for HRQOL assessment in clinical practice for detection and tracking of symptoms and as a communication aid has been proven . Hence CAT is especially beneficial for use in clinical routine as it comes with a reduced measurement error in the individual assessment. This characteristic of CAT increases the practicability of HRQOL assessment for clinical use in general, such as for informing physicians and guiding interventions. CAT has already proved efficient for a range of HRQOL constructs such as physical functioning  and fatigue .
The present paper describes the development of a CAT for role functioning (RF) which is a construct incorporated by all cancer-specific HRQOL instruments and comprises the ability of the individual to fulfil responsibilities typical for a specific age and social setting. Its assessment is quite complex due to the plurality of roles different persons define as relevant and due to the natural fluctuation of such roles over time. However, in the context of health outcome research, it has been argued that the focus needs to be on those aspects of role functioning which are influenced by health conditions and treatment . These include being productive in work and capable of caring for oneself and having a role in immediate and extended social networks . The operationalization of this definition differs across instruments but usually they separate occupational (e.g. work) from social aspects of functioning and assess them on distinct domains. This approach, for example, is applied by the two most widely used HRQOL questionnaires in oncology, the FACT-G (Functional Assessment of Cancer Therapy – General)  and the EORTC QLQ-C30 (European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30)  as well as by the widely used generic (i.e. non disease-specific) SF-36 (Short-Form 36) .
Despite agreement on the overall concept the issues that typically are covered in RF measures are quite diverse, ranging from questions on work efficiency to questions on illness acceptance. This is also what poses a special challenge to the development of a CAT for RF as IRT-based measures mostly are based on unidimensional item banks. Although multidimensional CATs can be done, they are highly complex and hence unidimensional structures are usually preferred.
Concurrently, very restricted definitions of RF, for example focusing on physical limitations only or very specific questions such as on reduction in working hours are considered a weakness of existing RF measures .
So far, three item banks measuring RF have been successfully developed. The National Institutes of Health-funded PROMIS (Patient Reported Outcomes Measurement Information System) initiative has developed two item banks for RF, one measuring the ability to participate in social roles and one measuring the satisfaction with social roles . Anatchkova et al.  developed an item bank for an RF-CAT which comprises occupational, social and family issues and which showed a sufficiently unidimensional structure. All of these item banks are generic and designed to be applicable in a broad range of different health conditions.
The present paper describes the development of a cancer-specific CAT for RF and is part of a larger EORTC Quality of Life Group’s (QLG) project whose goal is to generate a CAT version of the QLQ-C30.
The original QLQ-C30 RF Scale consists of two items (“Were you limited in doing either your work or other daily activities?” and “Were you limited in pursuing your hobbies or other leisure time activities?”). These items concern the domains “work or other daily activities” and “hobbies or other leisure time activities”, respectively, which are also described in the WHO International Classification of Functioning, Disability and Health (ICF) . Thus, these are the domains the new item bank should cover.
Develop a cross-culturally relevant and appropriate item list for the assessment of RF.
Develop of an item response theory (IRT) calibrated item bank for RF.
Evaluate the performance of the item bank in CAT simulations using real and simulated data.
EORTC item bank development is based on the EORTC guidelines for module development  and comprises four phases, namely defining the conceptual framework and conducting a literature search (phase I), operationalization (phase II), pre-testing (phase III) and field-testing and item bank calibration (phase IV). As with EORTC QLG modules these phases include pre-defined development steps and employ a multilingual and cross-cultural approach. The four development phases are summarised below. For further details on the general approach in phase I-III please refer to Petersen et al. 2010 , and for phase IV to Petersen et al. 2011 and 2012 [15, 16].
Conceptual framework and subdomains
In order to ensure that the new RF-item bank is comparable with data collected with the existing (static) version of the QLQ-C30, it needs to cover the same aspects as the QLQ-C30 RF Scale. The item bank should extend the measurement continuum, i.e. allow for the assessment of a broader range of severity of impairment, and increase measurement precision. In addition, the items should fit a unidimensional model in order to be included in the final item bank. The WHO ICF differentiates between limitations in activity and restrictions in participation. To reflect the RF construct as defined within the QLQ-C30 we decided to focus on limitations in activity and considered aspects of participation to be assessed by the social functioning domain of the QLQ-C30.
Phase I - Literature search
Phase I involved a literature search to collate existing items measuring RF. Searches were applied to the following databases: PubMed, EORTC Item Bank (http://groups.eortc.be/qol/item-bank), ProQolid (https://eprovide.mapi-trust.org/), Psyndex and PsyndexPlus. The search was conducted in September, 2008 applying combinations of the following free text and MeSH-terms: neoplasm*, cancer, role, social, daily, function*, well-being, limitation.
Phase II - Operationalization
The item list compiled in phase I was refined according to pre-defined selection steps. In each selection step two independent reviewers performed the ratings, which were then compared and discussed in case of disagreement. A third reviewer was involved in case of disagreement, ratings were discussed and then a majority decision was made. Reviewers had expertise in HRQOL, CAT and/or clinical oncology. First, items that were redundant, not compatible with the QLQ-C30 item style, or that assessed issues outside of the scope of conceptual framework were eliminated (step 1). Based on the remaining items, new items in the style of the QLQ-C30 (i.e. a question with a one-week recall period, assessing severity of impairment on a 4-point Likert scale from 1-not at all to 4-very much), were developed (step 2). Step 3 comprised another redundancy rating and a rating of item relevance to the RF construct. In step 4, the remaining items were rated for difficulty (i.e., the level of RF being assessed). Subsequently, they were subjected to QLG internal expert reviews (step 5) before being sent out for international expert reviews (step 6) on the items’ relevance for the assessment of RF, redundancy, clarity, and appropriateness.
Phase III - Pre-testing
To ensure content validity and the appropriateness of the items for the target population the preliminary item list was pre-tested in an international sample of cancer patients. Inclusion criteria were a cancer diagnosis, age ≥18 years, sufficient command of respective national language, no overt cognitive impairments, and informed consent. Translations were done according to published guidelines by the Translation Office of the EORTC Quality of Life Department . Based on patient feedback, the content and wording of the item list was refined and a preliminary item list to be used in field testing was created.
Phase IV - Field testing and calibration of the item bank
Sample and procedure
The preliminary item list was field-tested in an international sample of cancer patients. Inclusion criteria were the same as in phase III. We aimed at a heterogeneous sample of at least 1,000 patients, which is sufficiently large for the purposes of item calibration [18, 19]. Patients were approached in different oncology treatment settings (e.g., in-patient and outpatient; curative and palliative treatment) in order to cover a broad range of socio-demographic and clinical characteristics as well as different levels of RF impairment. In addition to the preliminary item list, patients completed the QLQ-C30 and answered questions on item relevance, clarity, and appropriateness, which were also presented paper-pencil based.
Evaluation of dimensionality and local dependence
The items were evaluated to determine if they met the requirements of unidimensionality and local independence using exploratory and confirmatory factor analyses. We were also interested in the potential overlap between the constructs RF and physical functioning (PF). As all patients had completed the QLQ-C30 in phase IV data collection we were able to investigate the factor structure of the new RF items and the physical functioning (PF) items of the QLQ-C30.
Eigenvalues, root mean square error of approximation (RMSEA) <0.10, the Tucker-Lewis Index (TLI) >0.90 and the Comparative Fit Index (CFI) >0.90 were used as criteria in the evaluation of factor structure and model fit [20, 21]. Residual correlations >0.20 served as indicators of local dependence (LD) .
Item bank calibration and evaluation of item fit
Items were checked for monotonicity, i.e. whether the cumulative probability of choosing a given response category or a higher category is non-decreasing with increasing IRT scores, i.e. the better RF, the more likely a response reflecting higher RF should be given. This was done by comparing the average item scores with the sum of the rest scores. Then items were calibrated to a generalized partial credit model (GPCM) , a model which allows estimating a discrimination (slope) parameter for each item (i.e. the item’s ability to discriminate between people) and a set of threshold parameters (i.e. the locations on the continuum where the item’s response options are most likely to be endorsed). To assess item fit, S-Χ2 fit statistics [24, 25], the difference between expected and observed responses (bias) and infit and outfit mean-squares (MnSq) were used . Bias is indicated by a root mean square error (RMSE) of ≥1, which would correspond to a difference of one response category. Concerning MnSq-values, primarily large infit and outfit, i.e. >1.3, were regarded as problematic as they indicate poor agreement between observed and expected responses . In addition, to make infit and outfit values less dependent on sample size and variation of responses they can be t-transformed to approximately standard normal distribution. Values outside ±2 (1.96) may be regarded as possibly problematic (95 % CI), and e.g. outside ±2.6 (99 %) as problematic, and outside 3.3 (99.9 % CI) as clearly problematic.
Differential item functioning
The items then were tested for differential item functioning (DIF), i.e. if items perform differently in certain sociodemographic and clinical subgroups. This was done using ordinal logistic regression [28–30]. Group variables were age, gender, country, cancer site, cancer stage, current treatment, living with a partner/alone, level of education, working/retired/other. Subsequently, for items with DIF it was tested if it affects parameter estimates. The method compares the RF scores obtained with the model which does not account for DIF with a model which does. If the RF scores differ substantially, defined as a difference larger than the median standard error for the RF estimates, this would indicate practically problematic DIF, also termed “salient scale-level differential functioning” [15, 28, 31].
Evaluation of measurement properties
Finally, the item bank’s performance for CAT measurement was assessed using real and simulated data. CAT simulations to evaluate measurement precision were done using Firestar and were based on the collected data (N = 1023). We simulated CATs asking an increasing number of items starting with one and ending with 9. We estimated the RF score based on these CATs, and compared these scores with the score based on all 10 items. As starting item we used the QLQ-C30 RF item with the highest average information. The Expected A Priori (EAP) method was applied for latent trait (theta/θ) estimation.
To evaluate possible savings in sample size, relative validity (RV) of the CATS compared to the QLQ-C30 RF scale in detecting expected group differences was calculated . The RV is the ratio of two test statistics for comparing two (known) groups. We used the t-test statistic for each of the CATs as the numerator and the t-test for the QLQ-C30 RF scale as the denominator – hence an RV >1 indicates that the CAT has greater discriminating power than the QLQ-C30 scale. Known group variables (age, sex, stage, work, therapy, education) were tested if significant for either the CAT or the QLQ-C30 measures. If significant they were used for calculating RVs. This was done based on the collected data.
RV was also assessed on the basis of simulated data. We simulated responses to the items on the basis of RF scores sampled from normal distributions with different means. We compared groups of different sizes and different true effect sizes. For each of the possible settings we ran 2000 simulations. For more details on methods please refer to Petersen et al. 2011  and Petersen et al. 2012 . Statistical packages used were SAS, Parscale  and Mplus .
Phase I: Literature search and item collection
The literature search described previously yielded 122 items on RF from 16 questionnaires.
Phase II: Operationalization - development of items and expert reviews
Reviewers disagreed on 11–25 % of the items across the developmental steps 1–4. Consensus choices were guided by the policy that patients at different ages and with different diagnoses and stages need to be able to relate to the content of the items.
Upon completion of step 4 the items were reviewed by members of the QLG as well as external international experts. Potential project collaborators are approached at the bi-annual meetings of the QLG. These meetings are frequented by researchers from all over Europe as well as from non-European countries. In total, 14 reviewers from 6 countries (Austria, Australia, Denmark, the Netherlands, Taiwan, and the UK) were involved (steps 5–6). Hence, we were able to obtain reviews from different English speaking countries as well as from European and non-European countries. Based on reviews within the QLG 5 items were excluded as they were rated as not fitting the QLQ-C30 concept of RF, being too specific, or being redundant. Changes that were prompted by the external reviewers concerned clarity (7 items) and appropriateness (1 item). The wording of 4 items was changed accordingly and one item was deleted due to redundancy. The resulting preliminary item list for cross-cultural patient interviews comprised 12 items.
Phase III: Pre-testing
Sample phase III: Pre-testing (N = 41)
Sample phase IV: Field-testing (N = 1023)
Age in years (Mean ± SD)
Head & neck
Stage I + II
Stage III + IV
Married/Living with partner
More than 16 years
Thus, the provisional item list consisted of 12 items, 9 items covering “work or other daily activities” and 3 items the domain “hobbies or other leisure time activities” (difficulties: 4 easy, 5 moderate, 3 difficult) (see Table 1).
Phase IV: Field testing and calibration of item bank
Sample characteristics and descriptive analyses
In line with the guidelines  patients were recruited in the same countries as in phase III. Responses from 1,023 patients were obtained (mean age 61.6 ± 12.7; 52.8 % female) (details can be seen in Table 1). All 12 RF items were answered by 93.4 % of the sample, and only 3.2 % missed two or more items. The respondents generally showed high levels of RF with a mean score of 1.7 (1 = not at all, 2 = a little, 3 = quite a bit, 4 = very much). There was a clear ceiling effect (23 % of patients answered “not at all” to all items), thus patients with poor RF were underrepresented in the sample. However for every item at least 10 patient responses for each response category were present and, thus, response distributions were deemed adequate for analyses and calibration. Generally the patients had very few problems with answering these items (at most 0.4 % reported problems for each item).
Evaluation of dimensionality and local dependence
The requirements of unidimensionality and local independence were sufficiently met by all 12 items, so they could all be included in IRT analyses (RMSEA = 0.081, CFI = 0.987, TLI = 0.997, residual correlations <0.15). Furthermore, investigation of the factor structure of RF and PF items supported a 2-factor solution. PROMAX rotations indicated that, although items generally loaded strongly on both factors, PF items clearly loaded higher on one factor while RF items loaded higher on the other. Confirmatory analyses showed that a one factor solution would be possible according to TLI and CFI, but RMSEA was not completely satisfying with a value of 0.118 in the one factor CFA and improving significantly to 0.085 in a two factor CFA.
Evaluation of item fit
Items of phase III and phase IV including difficulties, subdomains, item parameters, and fit indices
Item fit p-value
Item 1: Have you been limited in completing your household tasks?
Item 2: Have you been limited in doing light housework (e.g. dusting or making the bed)?
Item 3: Have you been limited in doing physically demanding recreational activities (e.g., swimming or cycling)?
Item 4: Have you needed assistance in doing your work or daily activities?
Item 5: Have you been limited in doing light recreational activities (e.g., watching TV, playing cards, or reading)?a
Item 6: Have you been limited in doing minor household repairs and maintenance (e.g., changing a light bulb or hanging up a picture)?
Item 7: Have you been limited in taking care of personal or household financial affairs (e.g. paying bills)?ab
Item 8: Were you limited in doing either your work or other daily activities?bc
Item 9: Have you been limited in doing heavy housework (e.g., washing floors or vacuuming)?b
Item 10: Were you limited in pursuing your hobbies or other leisure time activities?bc
Item 11: Have you been limited in doing your work around the house?
Excluded in phase IV
Item 12: Have you been limited in doing the necessary shopping (e.g., for groceries or clothes)?
Excluded in phase IV
The GPCM converged for an 11-item model. RMSEs between 0.31 and 0.67 indicated some variation in the precision of predicting the item responses, but all were < 1 (i.e. < 1 response category), so were not highly problematic. Furthermore, raw infit values ranging between 0.76 and 1.05 were acceptable. Likewise in the acceptable range were t-transformed infit values ranging between −1.5 and 0.8, except for item ID 12 (see Table 2), for which infit was −4.4 indicating clear problems with redundancy. Therefore it was deleted from the model. Items 5 and 7 (item text can be seen in Table 2) showed reversed thresholds. Although the GPCM would allow the ordering of thresholds to vary across items, reversed thresholds may give rise to counter-intuitive item characteristic (e.g. for some levels of RF answering ‘a little’ or ‘very much’ would then both be more likely than answering ‘quite a bit’). As most responses were in the category “not at all” and then frequencies were decreasing over the categories with the least in “very much”, the disorder may be an artefact caused by having too few responses in these categories. Therefore, response categories were collapsed to two categories (“not at all” vs. rest of categories). Our strategy for dealing with distorted thresholds is based on the approach suggested by Andrich  as opposed to the approach suggested by Adams et al. .
Finally, this yielded 10 items with good fit indices (S-X2 p > 0.05, infits between 0.90 and 1.03, outfits between 0.59 and 0.93, and bias ≤0.03 (which is about one point on 0–100 scale). Item texts and fit indices are shown in Table 2.
DIF analyses showed that items 7–10 demonstrated significant DIF for different variables (details provided in Table 2). Detailed evaluations of the DIF effect indicated that it had only very minor impact on the estimation of RF-scores (all differences in RF scores were less than 0.1 and did not indicate salient scale-level differential functioning). Detailed evaluations of the DIF effect indicated that it had only very minor impact on the estimation of RF-scores; thus no items were deleted (results not shown).
Calibration and evaluation of final item bank
As no item had to be excluded due to DIF the final item bank contained 10 items which were calibrated within a GPCM.
Being able to maintain or rebuild ones usual life is an important aspect of coping with a chronic disease such as cancer. Thus, assessing the extent to which a person can fulfil his/her responsibilities is inevitable in the evaluation of oncologic treatments. Furthermore, issues of daily functioning are of utmost importance for cancer survivors, who as a result of rising incidence and decreasing mortality rates are a growing population. For some sites this population even comprises increasingly younger age groups [38, 39].
In HRQOL research in general there is an interest in CAT measures due to their potential of a more effective and precise assessment compared to traditional instruments. Longitudinal studies and monitoring programs in clinical routine may especially benefit from the flexibility of a CAT as they allow repeated assessments with changing sets of questions, supposedly reducing carelessness as well as the impression of redundancy when completing the questions.
In this paper we have reported on the development and initial testing of a CAT for RF within the QLQ-C30 measurement framework. The project resulted in a sufficiently unidimensional item bank comprising 10 items including the two original QLQ-C30 items. We investigated the potential overlap between RF and PF and found that, as expected, they are closely related. However, there is sufficient statistical support for the conceptual separation of the two constructs.
The RF item bank showed high precision for patients with high and moderate RF impairments. A two-item CAT showed higher measurement precision than the original QLQ-C30 RF scale, which also consists of two items. When using CAT with five or more items, there are possible savings of required sample size between 11 and 50 %.
In a previous study  based on other data, we had stated that there may be little gain with IRT scoring compared to sum scoring of the QLQ-C30 domains. However, within the EORTC QLG CAT project, we found significant differences in the potential gain from using IRT/CAT across the different domains. Some of the most significant gains were found for the 1 and 2-item scales of the QLQ-C30 (the RF scale being one of those), while for emotional functioning and fatigue CATs with ≤4 items, there was little difference between IRT scoring and sum scoring. Hence, our former conclusion, based on the physical functioning, the emotional functioning, and the fatigue domains only, may have been premature.
A major strength of RF-CAT is the standardized developmental procedure involving international experts from different fields as well as patients themselves enabled the best possible balance between requirements from a psychometric and a practical perspective. This of special importance as the QLQC30 RF scale is designed to assess role impairments in all kinds of cancer patients, i.e. a heterogeneous group concerning sociodemographic and clinical characteristics. Furthermore, as the item bank also comprises the original QLQ-C30 RF items, even data collected prior to this project can be IRT scored now which makes information from earlier and future studies comparable.
As other functioning domains assessed by the QLQ-C30, RF comprises different aspects of the construct, namely work and daily activities and leisure time activities. Within an assessment procedure, the CAT algorithm selects items based on the criterion of maximum information from a statistical perspective. However, to secure the RF construct of the QLQ-C30, content balancing can be included in the algorithm to ensure that the assessment includes both of these aspects of RF, i.e. item selection is based on statistical as well as on content-related reasoning.
A limitation of the item bank is the clear ceiling effect, i.e. it cannot discriminate well between patients with minor RF impairments, which might be of special interest when investigating survivor issues. Construction of well-fitting items relevant for these patients would clearly improve the item bank; however such a task may be difficult. There were no obvious candidate items from the 122 identified from 16 instruments in the original literature search, and therefore, additional work is needed to identify ways to phrase such items.
Although 10 items form a small item bank, our results suggest that a 2-item CAT based on the item bank may provide an improve measure of RF and enable sample size reductions compared to the static 2-item RF scale of the QLQ-C30. Considering that HRQOL assessments typically cover multiple domains, there is clear benefit from more concise assessment of each single domain. Another advantage of the IRT calibrated item bank is that it will also be possible to create fixed ‘short-forms’ for certain purposes (e.g. screening in clinical routine) without losing comparability with other data collected with other items from the same bank. It should noted that significant differences in estimated required sample sizes were observed, depending on whether known group comparisons were based on simulated or observed data. Hence, the actual gain in terms of sample size reduction may vary across studies, and measurement properties need to be evaluated in independent data. This is currently being done within the ongoing EORTC CAT clinical validation study. The EORTC CAT versions have been integrated into software and will be made available on the EORTC QLG website after completion of the current validation exercise. This will also, hopefully, promote the use of HRQOL assessment in daily clinical practice.
Ethics, consent and permissions
The study was approved by the ethics committees of the participating centres/countries. All included patients provided informed consent.
EORTC Quality of Life Group (grant number 0052007)
The work of Eva-Maria Gamper and Johannes Giesinger was partly funded by the Austrian Science fund (project numbers P26930-B24 and J3353).
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Wainer H. Computerized adaptive testing: A primer. 2nd ed. Hillsdale: Lawrence Erlbaum Associates; 2000.Google Scholar
- Howell D, Molloy S, Wilkinson K, Green E, Orchard K, Wang K, Liberty J. Patient-reported outcomes in routine cancer clinical practice: A scoping review of use, impact on health outcomes, and implementation factors. Ann Oncol. 2015;26:1846–58.View ArticlePubMedGoogle Scholar
- Oude Voshaar MA, Ten Klooster PM, Glas CA, Vonkeman HE, Krishnan E, van de Laar MA. Relative performance of commonly used physical function questionnaires in rheumatoid arthritis and a promis computerized adaptive test. Arthritis Rheumatol. 2014;66(10):2900–8.View ArticlePubMedGoogle Scholar
- Lai JS, Cella D, Choi S, Junghaenel DU, Christodoulou C, Gershon R, Stone A. How item banks and their application can influence measurement practice in rehabilitation medicine: A promis fatigue item bank example. Arch Phys Med Rehabil. 2011;92:S20–27.View ArticlePubMedPubMed CentralGoogle Scholar
- Sherbourne CD, Stewart AL, Wells KB. Role functioning measures. In: Stewar AL, Ware Jr JE, editors. Measuring functioning and well-being: The medical outcomes study approach. Durham: Duke University Press; 1992. p. 205–19.Google Scholar
- Goodman SH, Sewell DR, Cooley EL, Leavitt N. Assessing levels of adaptive functioning: The role functioning scale. Community Ment Health J. 1993;29:119–31.View ArticlePubMedGoogle Scholar
- Cella DF, Tulsky DS, Gray G, Sarafian B, Linn E, Bonomi A, Silberman M, Yellen SB, Winicour P, Brannon J, et al. The functional assessment of cancer therapy scale: Development and validation of the general measure. J Clin Oncol. 1993;11:570–9.PubMedGoogle Scholar
- Aaronson NK, Ahmedzai S, Bergman B, Bullinger M, Cull A, Duez NJ, Filiberti A, Flechtner H, Fleishman SB, de Haes JC. The european organization for research and treatment of cancer qlq-c30: A quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst. 1993;85:365–76.View ArticlePubMedGoogle Scholar
- McHorney CA, Ware Jr JE, Lu JF, Sherbourne CD. The mos 36-item short-form health survey (sf-36): Iii. Tests of data quality, scaling assumptions, and reliability across diverse patient groups. Med Care. 1994;32:40–66.View ArticlePubMedGoogle Scholar
- Hahn EA, DeWalt DA, Bode RK, Garcia SF, DeVellis RF, Correia H, Cella D. New english and spanish social health measures will facilitate evaluating health determinants. Health Psychol. 2014;33:490–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Anatchkova M, Rose M, Ware J, Bjorner JB. Evaluation of a role functioning computer adaptive test (rf-cat). Qual Life Res. 2013;22:1085–92.View ArticlePubMedGoogle Scholar
- WHO. Icf: International classification of functioning, disability and health/world health organization. Geneva: Author; 2001.Google Scholar
- Johnson C, Aaronson N, Blazeby J, Bottomly A, Fayers P, Koller M, Kulis D, Ramage J, Sprangers M, Velikova G, Young T. Guidelines for developing questionnaire modules. 2011. http://groups.eortc.be/qol/sites/default/files/archives/guidelines_for_developing_questionnaire-_final.pdf. Accessed 13 June 2012.
- Petersen MA, Groenvold M, Aaronson NK, Chie WC, Conroy T, Costantini A, Fayers P, Helbostad J, Holzner B, Kaasa S, Singer S, Velikova G, Young T. Development of computerised adaptive testing (cat) for the eortc qlq-c30 dimensions - general approach and initial results for physical functioning. Eur J Cancer. 2010;46:1352–8.View ArticlePubMedGoogle Scholar
- Petersen MA, Groenvold M, Aaronson NK, Chie WC, Conroy T, Costantini A, Fayers P, Helbostad J, Holzner B, Kaasa S, Singer S, Velikova G, Young T. Development of computerized adaptive testing (cat) for the eortc qlq-c30 physical functioning dimension. Qual Life Res. 2011;20:479–90.View ArticlePubMedGoogle Scholar
- Petersen MA, Aaronson Neil K, Arraras J, Chie WC, Conroy T, Costantini A, Giesinger J, Holzner B, King M, Singer S, Velikova G, Verdonck-de Leeuw IM, Young T, Groenvold M. The eortc computer-adaptive tests (cats) measuring physical functioning and fatigue exhibited high levels of measurement precision and efficiency. J Clin Epidemiol. 2013;66:330–9.View ArticlePubMedGoogle Scholar
- Koller M, Aaronson Neil K, Blazeby J, Bottomley A, Dewolf L, Fayers P, Johnson C, Ramage J, Scott N, West K. Translation procedures for standardised quality of life questionnaires: The european organisation for research and treatment of cancer (eortc) approach. Eur J Cancer. 2007;43:1810–20.View ArticlePubMedGoogle Scholar
- Muraki E, Bock RD. Parscale - irt based test scoring and item analysis for graded open-ended exercises and performance tasks. Chicago: Scientific Software International, Inc.; 1996.Google Scholar
- Choi SW, Cook KF, Dodd BG. Parameter recovery for the partial credit model using multilog. J Outcome Meas. 1997;1:114–42.PubMedGoogle Scholar
- Browne M, Cudeck R. Alternative ways of assessing model fit. Sociol Methods Res. 1992;21:230–58.View ArticleGoogle Scholar
- Kline R. Principles and practice of structural equation modeling. 2nd ed. New York: The Guilford Press; 2005.Google Scholar
- Bjorner Jakob B, Kosinski M, Ware John Jr E. Calibration of an item pool for assessing the burden of headaches: An application of item response theory to the headache impact test (hit). Qual Life Res. 2003;12:913–33.View ArticlePubMedGoogle Scholar
- Muraki E. A generalized partial credit model. In: van der Linden WJH, Hambleton RK, editors. Handbook of modern item response theory. Berlin: Springer; 1997. p. 153–68.View ArticleGoogle Scholar
- Orlando M, Thissen D. Likelihood-based item-fit indices for dichotomous item response theory models. Appl Psychol Meas. 2000;24:50–64.View ArticleGoogle Scholar
- Orlando M, Thissen D. Further examination of the performance of s-x2, an item fit index for dichotomous item response theory models. Appl Psychol Meas. 2003;27:289–98.View ArticleGoogle Scholar
- Bond TG, Fox CM. Applying the rasch model: Fundamental measurement in the human sciences ed 2. New Jersey: Lawrence Erlbaum Associates, Inc; 2007.Google Scholar
- Linacre JM. Sample size and item calibration stability. Rasch Measurement Transactions. 1994;7:328.Google Scholar
- Petersen MA, Giesinger JM, Holzner B, Arraras JI, Conroy T, Gamper EM, King MT, Verdonck-de Leeuw IM, Young T, Groenvold M. Psychometric evaluation of the eortc computerized adaptive test (cat) fatigue item pool. Qual Life Res. 2013;22:2443–54.View ArticlePubMedGoogle Scholar
- French A, Miller TR. Logistic regression and its use in detecting differential item functioning in polytomous items. J Educ Meas. 1996;33:315–32.View ArticleGoogle Scholar
- Petersen Morten A, Groenvold M, Bjorner Jakob B, Aaronson N, Conroy T, Cull A, Fayers P, Hjermstad M, Sprangers M, Sullivan M. Use of differential item functioning analysis to assess the equivalence of translations of a questionnaire. Qual Life Res. 2003;12:373–85.View ArticlePubMedGoogle Scholar
- Hart DL, Deutscher D, Crane PK, Wang YC. Differential item functioning was negligible in an adaptive test of functional status for patients with knee impairments who spoke english or hebrew. Qual Life Res. 2009;18:1067–83.View ArticlePubMedGoogle Scholar
- Fayers P, Machin D. Quality of life. The assessment, analyses and interpretation of patient-reported outcomes. 2nd ed. Chichester: Wiley; 2007.Google Scholar
- Muraki E. Irt based test scoring and item analysis for graded open-ended exercises and performance tasks. Chicago: Scientific Software, Inc.; 1996.Google Scholar
- Muthén LK, Muthén BO. Mplus user’s guide. 5th ed. Los Angeles, CA: Muthén & Muthén; 1998-2007.Google Scholar
- Gamper EM, Groenvold M, Petersen MA, Young T, Costantini A, Aaronson N, Giesinger JM, Meraner V, Kemmler G, Holzner B. The eortc emotional functioning computerized adaptive test: Phases i-iii of a cross-cultural item bank development. Psychooncology. 2013;23(4):397-403.Google Scholar
- Andrich D. An expanded derivation of the threshold structure of the polytomous rasch model that dispels any “threshold disorder controversy”. Educ Psychol Meas. 2013;73:78–124.View ArticleGoogle Scholar
- Adams RJ, Wu ML, Wilson M. The rasch rating model and the disordered threshold controversy. Educ Psychol Meas. 2012;72:547–73.View ArticleGoogle Scholar
- Siegel R, Desantis C, Jemal A. Colorectal cancer statistics, 2014. CA Cancer J Clin. 2014;64:104–17.View ArticlePubMedGoogle Scholar
- NCIN. Thyroid cancer trends by sex age and histological type. National Cancer Intelligence Network. 2014. http://www.ncin.org.uk/publications/data_briefings/thyroid_cancer_trends_by_sex_age_and_histological_type. Accessed 2 May 2016.
- Petersen MA, Groenvold M, Aaronson N, Brenne E, Fayers P, Nielsen JD, Sprangers M, Bjorner JB. Scoring based on item response theory did not alter the measurement ability of eortc qlq-c30 scales. J Clin Epidemiol. 2005;58:902–8.View ArticlePubMedGoogle Scholar