Background

Profile instruments are frequently used to assess health-related quality of life and other patient-reported outcomes. However, preference-based measures are required for health-economic cost-utility evaluations.

Results

Although regression-based approaches are commonly used to map from profile measures to preference measures, we show that this results in biased estimates because of regression to the mean.

Conclusions

Linking (scale-aligning) is proposed as an alternative.

Because depressive symptoms are a part of health-related quality-of-life (HRQOL) measures, measures of depression will be empirically associated with HRQOL. We discuss examples of published research where authors ignored or did not fully account for overlap between depressive symptom and HRQOL measures. Future researchers need to recognize when their models include conceptually similar variables on the same side or both sides of the equation. This awareness will lead to more accurate conclusions about the prognostic value of depression and other HRQOL measures for health care utilization, mortality, and other outcomes. It will also result in fewer incorrect claims about the effect of depression on HRQOL.

Minimal important differences (MIDs) for patient-reported outcomes (PROs) are often estimated by selecting a clinical variable to serve as an anchor. Then, differences in the clinical anchor regarded as clinically meaningful or important can be used to estimate the corresponding value of the PRO. Although these MID values are sometimes estimated by regression techniques, we show that this is a biased procedure and should not be used; alternative methods are proposed.

Purpose

Half of the 21-item Minnesota Living with Heart Failure Questionnaire (MLHFQ) response categories are labeled (0 = No, 1 = Very little, 5 = Very much) and half are not (2, 3, and 4). We hypothesized that the unlabeled response options would not be more likely to be chosen at some place along the scale continuum than other response options and, therefore, not satisfy the monotonicity assumption of simple-summated scoring.

Methods

We performed exploratory and confirmatory factor analyses of the MLHFQ items in a sample of 1437 adults in the Better Effectiveness After Transition-Heart Failure study. We evaluated the unlabeled response options using item characteristic curves from item response theory-graded response models for MLHFQ physical and emotional health scales. Then, we examined the impact of collapsing response options on correlations of scale scores with other variables.

Results

The sample was 46% female; 71% aged 65 or older; 11% Hispanic, 22% Black, 54% White, and 12% other. The unlabeled response options were rarely chosen. The standard approach to scoring and scores obtained by collapsing adjacent response categories yielded similar associations with other variables, indicating that the existing response options are problematic.

Conclusions

The unlabeled MLHFQ response options do not meet the assumptions of simple-summated scoring. Further assessment of the performance of the unlabeled response options and evaluation of alternative scoring approaches is recommended. Adding labels for response options in future administrations of the MLHFQ should be considered.