Preference-based scores provide an overall summary of health-related quality of life on a common metric.
Preference-based scores summarize multiple domains into a single score anchored at 0 (as bad as dead) and 1 (perfect or ideal health). The advantage of preference-based measures is their ability to prioritize health interventions by overall impact and to measure changes in health for comparative-effectiveness and cost-effectiveness analyses. For example, preference-based scores can help estimate the relative value of two treatments with different outcomes (see image below).
Different Types of Summary Measures
Different approaches are used to describe overall health-related quality of life. One strategy is to use a profile measure (e.g., the PROMIS-29). Profile measures assess multiple domains of health such as physical functioning, depression, and pain. Profile measures then provide multiple scores – one for each domain assessed. Each score provides information to understand health in detail and together, the scores provide a broader, more comprehensive description of health.
A second strategy is to use a global measure (e.g., PROMIS Global Health). A global measure combines information about multiple domains of health into a summary score. For example, the PROMIS Global Health scale produces a score for physical function that is based on 4 items about fatigue, pain, and physical function and a score for mental health that is based on 4 items about overall perceptions of quality of life, mental health, social activities and relationships. Global measures can be helpful when comparing large groups. They may be less helpful in providing actionable information for an individual patient in a clinical encounter. It is also difficult to tease apart the contribution of specific domains like fatigue versus pain, particularly when interpreting change over time. Different global health measures utilize different metrics unlike preference-based scores which all use 0 to 1.
A third strategy is to use a preference-based score. A preference-based score begins with the assessment of multiple domains of health. However, unlike a profile, a preference-based score combines multiple domains into a single number that ranges from 0 (“dead”) to 1 (“full health”). This score quantifies the value that individuals place on different states of health. Preference-based scores represent explicit trade-offs between different levels of health in different domains. Preference-based scores can be used in cost-utility analyses and to estimate quality-adjusted life years (QALYs). The images below show examples of their use.
Multiple methods are used to construct preference-based scores including time tradeoff, standard gamble, and others (e.g., discrete choice). Scores are based on preferences for different health states (i.e., different combinations of health profile scores) and are often developed by combining responses from a large nationally-representative sample. Preference-based scores are also called health utility scores when certain constraints are met. A 2020 JAMA publication provides a short overview of health state utility assessment. Learn more>>
Both profile and preference-based scores are important in monitoring health outcomes. It is important to note that preference-based scores are not reflective of preferences for an individual patient and should not be used for individual care decision making; much like democracy, where the aggregated outcomes of an election do not necessarily represent any individual’s preferences, an aggregated preference-based score does not necessarily represent how an individual person would make treatment decisions.
PROMIS-Preference (PROPr) Score
The PROMIS-Preference (PROPr) score is a generic, societal, preference-based summary score that has demonstrated validity (see Hanmer 2018). It is based on PROMIS scores for Cognition (Cognitive Function or Cognitive Function Abilities), Depression, Fatigue, Pain Interference, Physical Function, Sleep Disturbance, and Ability to Participate in Social Roles and Activities. Learn more >>
PROPr Requires 7 PROMIS Domains
An analyst can calculate a PROPr score with scores from 7 PROMIS domains. There are three options for obtaining the necessary data:
1. Administer computer adaptive tests (CATs) for:
- PROMIS Cognitive Function v2.0 or Cognitive Function – Abilities v2.0
- PROMIS Depression v1.0
- PROMIS Fatigue v1.0
- PROMIS Pain Interference v1.0 or v1.1
- PROMIS Physical Function v1.0, v1.1, v1.2 or v2.0
- PROMIS Sleep Disturbance v1.0
- PROMIS Ability to Participate in Social Roles and Activities v2.0
2. Administer the PROMIS-29+2 Profile v2.1 (PROPr). This is the PROMIS-29 Profile v2.1 and two items from Cognitive Function – Abilities v2.0
- PC6r: In the past 7 days, I have been able to concentrate. 1=Not at all, 2=A little bit, 3=Somewhat, 4=Quite a bit, 5=Very much
- PC27r: In the past 7 days, I have been able to remember to do things, like take medicine or buy something I needed. 1=Not at all, 2=A little bit, 3=Somewhat, 4=Quite a bit, 5=Very much
3. Administer the PROMIS-29 Profile v2.1 and a PROMIS Cognitive Function – Abilities or PROMIS Cognitive Function short form (e.g., 4a).
The PROMIS-Preference score was constructed using the Cognitive Function - Abilities Subset v2.0 Item Bank. To get a cognition estimate to calculate a PROPr score, you can either use Cognitive Function - Abilities Subset v2.0 or Cognitive Function v2.0. Since both measures are co-calibrated using IRT, their scores are comparable.
Access PROPr Scoring Software Code
PROPr is calculated from the scores for the 7 PROMIS domains. PROMIS domain scores can be calculated using the HealthMeasures Scoring Service. The HealthMeasures Scoring Service export includes PROMIS T-scores and theta values. For PROPr, you will use the theta values. See Scoring Instructions for more information>>
Once you have theta scores, apply SAS or R code to calculate a PROPr score. The code is named “MAUT” (Multi-Attribute Utility Theory) and available at https://github.com/janelhanmer/PROPr
Other PROMIS Preference-based Summary Scores
EQ-5D preference-based health index scores can be estimated from the PROMIS Global Health scale. This uses scoring for the United States. To learn more, see Thompson et al et al (2017). Scoring instructions are included in the PROMIS Global Scoring Manual.
EQ-5D preference-based health index scores can also be estimated from the PROMIS-29 profile. This uses scoring for the France, Germany, and the United Kingdom. See Klapproth et al (2020).
Health Utilities Index Mark 3 (HUI-3) preference scores can be estimated from the PROMIS-29 v2.0 Profile (preferred) or PROMIS Global Health scale. For more information and scoring equations, see Hays et al (2016).
Neuro-QoL™ Preference-based Score
A preference-based score for individuals with multiple sclerosis based on Neuro-QoL cognitive function, depression, fatigue, mobility, upper extremity function, and ability to participate in social roles and activities measures was published in 2020 (see Matza et al). Scoring code in R and SAS is available in the publication’s online Appendices 2 and 3.
Matza, L. S., Phillips, G., Dewitt, B., Stewart, K. D., Cella, D., Feeny, D., . . . Revicki, D. A. (2020). A Scoring Algorithm for Deriving Utility Values from the Neuro-QoL for Patients with Multiple Sclerosis. Medical Decision Making, 40(7), 897-911. https://pubmed.ncbi.nlm.nih.gov/33016238/