Dataset Information

Confirmatory Factor Analysis and Measurement Invariance of the Functional Assessment of Cancer Therapy Lung Cancer Utility Index (FACT-LUI).

ABSTRACT: Background. A portion of the Functional Assessment of Cancer Therapy-Lung (FACT-L) instrument contributed to a previously published utility index, the FACT Lung Utility Index or FACT-LUI. Six FACT items representing lung cancer quality of life covered fatigue, pain, dyspnea, cough, anxiety, and depression. Two FACT items had been previously combined by the index authors into one for nausea and/or appetite loss, resulting in 7 final domains. Methods. The objective was to perform measurement invariance testing within a confirmatory factor analysis (CFA) framework to support the feasibility of using the FACT-LUI for non-preference-based psychometric applications. The original index patients comprised group 1, and similar FACT patient data (n = 249) from another published study comprised group 2. One 2-factor model and two 1-factor CFA models were evaluated to assess measurement invariance across groups, using varying degrees of item parceling and a small number of residual covariances, all justified by the literature. Results. The 1-factor models were most optimal. A 1-factor model with 1 pair of items parceled showed invariance to the partial scalar level using usual fit criteria across groups, requiring 2 unconstrained intercepts. A 1-factor model with 3 pairs of justified parcels showed full configural, metric, and scalar invariance across groups. Conclusions. The FACT-LUI items fit a partially to fully invariant 1-factor model, suggesting feasibility for non-preference-based applications. Implications. Results suggest useful incorporation of the FACT-LUI into clinical trials with no substantial increased respondent burden, allowing preference-based and other psychometric applications from the same index items.

Highlights

This work suggests that in addition to being originally designed for use as a utility index, the 7 FACT-LUI items together also fit simple CFA and measurement invariance models. This less expected result indicates that these items as a group are also potentially useful in non-preference-based applications.Clinical trials can make for challenging decisions concerning which patient-reported outcome measures to include without being burdensome. However, the literature suggests a need for improved reporting of quality of life in lung cancer in particular as well as cancer in general. Inclusion of more disease-specific items such as the FACT-LUI may allow for information gathering of both preference-based and non-preference-based data with less demand on patients, similar to what has been done with some generic instruments.

SUBMITTER: Swan JS

PROVIDER: S-EPMC10388633 | biostudies-literature | 2023 Jul-Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Confirmatory Factor Analysis and Measurement Invariance of the Functional Assessment of Cancer Therapy Lung Cancer Utility Index (FACT-LUI).

Swan J Shannon JS Langer Michelle M MM

MDM policy & practice 20230727 2

<b>Background.</b> A portion of the Functional Assessment of Cancer Therapy-Lung (FACT-L) instrument contributed to a previously published utility index, the FACT Lung Utility Index or FACT-LUI. Six FACT items representing lung cancer quality of life covered fatigue, pain, dyspnea, cough, anxiety, and depression. Two FACT items had been previously combined by the index authors into one for nausea and/or appetite loss, resulting in 7 final domains. <b>Methods.</b> The objective was to perform mea ...[more]

PMID: 37529767

Similar Datasets

Project description:IntroductionUtility instruments are used to assess patients' health-related quality of life for cost-utility analysis (CUA). However, for cancer patients, the dimensions of generic utility instruments may not capture all the information relevant to the impact of cancer. Cancer-specific utilities provide a useful alternative. Under the auspices of the Multi-Attribute Utility in Cancer Consortium, a cancer-specific utility algorithm was derived from the FACT-G. The new FACT-8D contains eight dimensions: pain, fatigue, nausea, sleep, work, support from family/friends, sadness, and worry health will get worse. The aim of the study was to obtain a Canadian value set for the FACT-8D.MethodsA discrete choice experiment was administered to a Canadian general population online panel, quota sampled by age, sex, and province/territory of residence. Respondents provided responses to 16 choice sets. Each choice set consisted of two health states described by the FACT-8D dimensions plus an attribute representing survival duration. Sample weights were applied and the responses were analyzed using conditional logistic regression, parameterized to fit the quality-adjusted life year framework. The results were converted into utility weights by evaluating the marginal rate of substitution between each level of each FACT-8D dimension with respect to duration.Results2228 individuals were recruited. The analysis dataset included n = 1582 individuals, who completed at least one choice set; of which, n = 1501 completed all choice sets. After constraining to ensure monotonicity in the utility function, the largest decrements were for the highest levels of pain (- 0.38), nausea (- 0.30), and problems doing work (- 0.23). The decrements of the remaining dimensions ranged from - 0.08 to - 0.18 for their highest levels. The utility of the worst possible health state was defined as - 0.65, considerably worse than dead.ConclusionsThe largest impacts on utility included three generic dimensions (i.e., pain, support, and work) and nausea, a symptom caused by cancer (e.g., brain tumours, gastrointestinal tumours, malignant bowel obstruction) and by common treatments (e.g., chemotherapy, radiotherapy, opioid analgesics). This may make the FACT-8D more informative for CUA evaluating in many cancer contexts, an assertion that must now be tested empirically in head-to-head comparisons with generic utility measures.

Project description:BackgroundThe Short Form Health Survey (SF-12v2) is an increasingly popular measure of health-related quality of life (HRQoL) in Singapore. In order to examine whether the SF-12v2 was appropriate for use in the population, the factor structure and validity of the English, Mandarin, and Malay versions were assessed in a representative sample of the general population of Singapore.Methods6126 respondents were recruited for the Singapore Mental Health Study 2016 (SMHS 2016), a cross-sectional and population-based survey. Confirmatory factor analyses (CFA) were conducted to examine the fit of a two-factor model for the SF-12v2 within a representative sample and amongst the different language (English, Mandarin, Malay) subgroups. Multiple-group CFAs (MGCFA) were conducted to test measurement invariance across the different languages, ethnicities, and chronic illnesses subgroups. CFA-generated latent factor scores (FSCORE command in MPlus) were also compared with the composite scores derived from the developer's scoring method via correlations. Sociodemographic correlates of the latent physical and mental health scores were explored.ResultsCFA results within the full sample supported a two-factor model (RMSEA = 0.044; CFI = 0.991; TLI = 0.988; SRMR = 0.044) in which physical functioning, role physical, bodily pain and general health items loaded onto a latent physical health factor, while role emotional, mental health, social functioning, and vitality items loaded onto a latent mental health factor. Physical and mental health factors were allowed to correlate, unlike the developer's orthogonal scoring method. All standardized loadings were high and statistically significant. Both factors had high internal consistency. CFA within subsamples of English, Mandarin, and Malay languages indicated similar findings. MGCFA results indicate that measurement invariance held across the different languages, ethnicities, and those with and without chronic illnesses.ConclusionThe present study identified a two-factor (physical and mental health) structure within the general population and amongst the three different languages and demonstrated the measurement invariance of SF-12v2 across different subgroups. Findings indicate that algorithm-derived PCS and MCS should be interpreted with caution as they may result in inaccurate conclusions regarding the relationships between HRQoL and its correlates. Future studies using the SF-12v2 within the general population of Singapore should consider utilizing the factor structure put forth in the present study to obtain more appropriate estimates of HRQoL.

Project description:ObjectiveTo develop a mapping algorithm for generating the Short Form Six-Dimension (SF-6D) utility score based on the Functional Assessment of Cancer Therapy-Lung (FACT-L) of lung cancer patients.MethodsData were collected from 625 lung cancer patients in mainland China. The Spearman rank correlation coefficient and principal component analysis were used to evaluate the conceptual overlap between the FACT-L and SF-6D. Five model specifications and four statistical techniques were used to derive mapping algorithms, including ordinary least squares (OLS), Tobit and beta-mixture regression models, which were used to directly estimate health utility, and ordered probit regression was used to predict the response level. The prediction performance was evaluated using the correlations between the root mean square error (RMSE), mean absolute error (MAE), concordance correlation coefficient (CCC), Akaike information criterion (AIC) and Bayesian information criterion (BIC) and the observed and predicted SF-6D scores. A five-fold cross-validation method was used to test the universality of each model and select the best model.ResultsThe average FACT-L score was 103.024. The average SF-6D score was 0.774. A strong correlation was found between FACT-L and SF-6D scores (ρ = 0.797). The ordered probit regression model with the total score of each dimension and its square term, as well as age and sex as covariates, was most suitable for mapping FACT-L to SF-6D scores (5-fold cross-validation: RMSE = 0.0854; MAE = 0.0655; CCC = 0.8197; AEs > 0.1 (%) = 53.44; AEs > 0.05 (%) = 21.76), followed by beta-mixture regression for direct mapping. The Bland‒Altman plots showed that the ordered probit regression M5 had the lowest proportion of prediction scores outside the 95% agreement limit (-0.166, 0.163) at 4.96%.ConclusionsThe algorithm reported in this paper enables lung cancer data from the FACT-L to be mapped to the utility of the SF-6D. The algorithm allows the calculation of quality-adjusted life years for cost-utility analyses of lung cancer.

Dataset Information

Confirmatory Factor Analysis and Measurement Invariance of the Functional Assessment of Cancer Therapy Lung Cancer Utility Index (FACT-LUI).

Highlights

Publications

Confirmatory Factor Analysis and Measurement Invariance of the Functional Assessment of Cancer Therapy Lung Cancer Utility Index (FACT-LUI).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets