Missing data due to attrition present a challenge for the assessment and interpretation of change and response shift in HRQL outcomes. The objective was to handle such missingness and to assess response shift and ‘true change’ with the use of an attrition-based multigroup structural equation modeling (SEM) approach.
Functional limitations and health impairments were measured in 1,157 cancer patients, who were treated with palliative radiotherapy for painful bone metastases, before [time (T) 0], every week after treatment (T1 through T12), and then monthly for up to 2 years (T13 through T24). To handle missing data due to attrition, the SEM procedure was extended to a multigroup approach, in which we distinguished three groups: short survival (3–5 measurements), medium survival (6–12 measurements), and long survival (>12 measurements).
Attrition after third, sixth, and 13th measurement occasions was 11, 24, and 41 %, respectively. Results show that patterns of change in functional limitations and health impairments differ between patients with short, medium, or long survival. Moreover, three response-shift effects were detected: recalibration of ‘pain’ and ‘sickness’ and reprioritization of ‘physical functioning.’ If response-shift effects would not have been taken into account, functional limitations and health impairments would generally be underestimated across measurements.
The multigroup SEM approach enables the analysis of data from patients with different patterns of missing data due to attrition. This approach does not only allow for detection of response shift and assessment of true change across measurements, but also allow for detection of differences in response shift and true change across groups of patients with different attrition rates.