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.

Keywords: factor analysis; health-related quality of life measurement; preference-based measurement; psychometrics; structural equation modeling; utility index.

Figure 1

Model Specifications - Model “A” has 2-factors and 1-parcel; Model “B” has 1-parcel and 1-factor, and Model “C” has 3 parcels and 1-factor for CFA of the FACT-LUI index items. G1 refers to Group 1 where the original FACT-LUI nausea and/or appetite loss item was assumed to act as a parcel. G2 refers to Group 2 where original separate FACT items for nausea and appetite were parceled. The symbol ε refers to item residual variance. The 2-Factor model is based on literature as described in text.

Figure 2.

Measurement invariance parameter estimates for 1-parcel 2-factor model (Model “A”). Standardized estimates are shown for interpretability. Any inequalities are due to the factor and variable variances which are used for standardization and are being allowed to be unequal while unstandardized loadings are constrained to equality in the model. Residual variances (ellipses) are seen below each variable. 2-headed arrows and estimates show allowed residual covariances (see text). Each pair of parameter estimates show Group 1 results atop Group 2. Non-invariant (unconstrained freely estimated) intercepts according to Satorra-Bentler adjusted χ² difference tests are reported in a box below the affected variable. Abbreviated fit statistics are shown with each invariance testing stage. Complete fit details are shown in Table 3.

Figure 3.

Measurement invariance parameter estimates for 1-parcel 1-factor model (Model “B”). Abbreviated fit statistics (see also Table 3), standardized parameter estimates and allowed residual covariances as described in text are shown.

Figure 4

Measurement invariance parameter estimates for 3-parcel 1-factor model (Model “C”). Abbreviated fit statistics (See also Table 3) and standardized parameter estimates are shown.