A non-compensatory analysis of quality of life in breast cancer survivors using multivariate hidden Markov modeling

Health-related quality of life (HRQoL) is a multidimensional concept comprising multiple domains such as physical, emotional, and social well-being. Many analyses use a sum score to represent the construct. However, this approach implies that gain in one domain can compensate for a deficit in another, and thus such analyses may not capture HRQoL profiles. Additionally, within-individual change over time, such as improvement in one domain but deterioration in another, may not be detected. The objectives of this research are to demonstrate the utility of a non-compensatory approach by (1) evaluating this approach applied to HRQoL data, and (2) comparing the approach to a compensatory method.

Data from a sample of 653 breast cancer survivors (BCS) provided five measurement time points over 18 months. We analyzed the scores from five domains on the FACT-B questionnaire (physical, functional, social, and emotional well-being and breast cancer-related concerns) using the multivariate hidden Markov model (MHMM), a non-compensatory approach that identifies different HRQoL states and associated BCS subgroups and their trajectories.

The MHMM delineated six states. States 1 and 2 had low well-being scores across all domains, with state 2 slightly better than state 1. States 3 and 4 had similar overall HRQoL scores, but different profiles with compensation occurring across the domains of both physical and social well-being. States 5 and 6 had almost identical overall scores with compensation occurring between the domains of both social and emotional well-being. Over time, states 3–6 mostly “communicated” with each other (with moderate probabilities of transitioning between states). Compensation across domains could mask subtle changes occurring in BCS. We found that a trend analysis using both compensatory and non-compensatory approaches showed improvement in the HRQoL in BCS over time.

The non-compensatory analysis of FACT-B shows differential profiles and trajectories in the HRQoL of BCS not captured by the sum score or one-domain-at-a-time approach.