Cross-sectional validation of the PROMIS-Preference scoring system

Abstract

ObjectivesThe PROMIS-Preference (PROPr) score is a recently developed summary score for the Patient-Reported Outcomes Measurement Information System (PROMIS). PROPr is a preference-based scoring system for seven PROMIS domains created using multiplicative multi-attribute utility theory. It serves as a generic, societal, preference-based summary scoring system of health-related quality of life. This manuscript evaluates construct validity of PROPr in two large samples from the US general population.MethodsWe utilized 2 online panel surveys, the PROPr Estimation Survey and the Profiles-Health Utilities Index (HUI) Survey. Both included the PROPr measure, patient demographic information, self-reported chronic conditions, and other preference-based summary scores: the EuroQol-5D (EQ-5D-5L) and HUI in the PROPr Estimation Survey and the HUI in the Profiles-HUI Survey. The HUI was scored as both the Mark 2 and the Mark 3. Known-groups validity was evaluated using age- and gender-stratified mean scores and health condition impact estimates. Condition impact estimates were created using ordinary least squares regression in which a summary score was regressed on age, gender, and a single health condition. The coefficient for the health condition is the estimated effect on the preference score of having a condition vs. not having it. Convergent validity was evaluated using Pearson correlations between PROPr and other summary scores.ResultsThe sample consisted of 983 respondents from the PROPr Estimation Survey and 3,000 from the Profiles-HUI survey. Age- and gender-stratified mean PROPr scores were lower than EQ-5D and HUI scores, with fewer subjects having scores corresponding to perfect health on the PROPr. In the PROPr Estimation survey, all 11 condition impact estimates were statistically significant using PROPr, 8 were statistically significant by the EQ-5D, 7 were statistically significant by HUI Mark 2, and 9 were statistically significant by HUI Mark 3. In the Profiles-HUI survey, all 21 condition impact estimates were statistically significant using summary scores from all three scoring systems. In these samples, the correlations between PROPr and the other summary measures ranged from 0.67 to 0.70.ConclusionsThese results provide evidence of construct validity for PROPr using samples from the US general population.

Measures for the Assessment of Pain in Adults

“…One such symptom cluster that is gaining attention in both adult and pediatric chronic pain is remembered by the acronym “SPACE” (sleep, pain, affect, cognitive dysfunction, and energy/fatigue) . SPACE can be efficiently assessed using a combination of PROMIS short‐form measures (eg, sleep‐related impairment, pain intensity, anxiety and depression, cognition, and fatigue scales) or by using one of the PROMIS profiles such as the PROMIS 29+2 , which contains scales assessing each of the elements within SPACE. This symptom cluster can also be assessed using a combination of legacy measures for each symptom, which have been reviewed elsewhere (eg, the Pittsburg Sleep Quality Index, the MBM, the PD‐Q [reviewed above], the HADS, the Multidimensional Inventory of Subjective Cognitive Impairment, and the Multidimensional Fatigue Inventory).…”

The quality of life index: a pilot study integrating treatment efficacy and quality of life in oncology

“…In the current pilot study, the PROPr utility score was calculated at three time points to generate an estimate of the QALYs experienced by the patient during the course of treatment 13 , 14 . Patients’ actual QALY was calculated by estimating the PROPr score at each measurement time-point, calculating the QALYs experienced during the intervening time period, and then summing the QALYs across treatment (baseline->pre-surgery + pre-surgery->post-surgery) (Fig.…”

Delivering a Post-Partum Weight Loss Intervention via Facebook or In-Person Groups: Protocol for a Randomized Feasibility Pilot Trial

“…After the baseline visit, staff will email participants a link to complete a survey via Research Electronic Data Capture (REDCap) [36]. The survey is designed to take 30 min to complete and includes measures of demographics; reproductive history [37], including gestational weight gain during the index pregnancy [38]; previous postpartum weight loss attempts [39]; quality of life [40,41]; infant feeding [42,43]; social support for diet and physical activity changes [44]; and social media use [45]. Participants will also report their Facebook use habits, including what device(s) they used to access Facebook, what proportion of use was from their smartphone (vs other devices), and the extent to which other people used Facebook on their phone [46].…”

“…At 6 months, participants will also complete an online survey that includes assessment of current pregnancy, depressive symptoms [34], quality of life [40,41], infant feeding [42,43], social support for diet and physical activity changes [44], social media use [45], group cohesion [68,69], contamination, and intervention acceptability [30]. Participants will also report app-tracked time on Facebook over the past 7 days and their Facebook use habits, including what device(s) they used to access Facebook, what proportion of use was from their smartphone (vs other devices), the extent to which other people used Facebook on their phone [46], and whether they consciously changed their Facebook use as a result of becoming aware of the time spent on Facebook and, if so, in what way [70].…”

“…Research staff will provide participants with instructions on how to remove the MyFitnessPal and time tracking apps from their phones. We will email participants a link to complete an online survey that includes measures of current pregnancy, depressive symptoms [34], quality of life [40,41], infant feeding [42,43], social support for diet and physical activity changes [44], and social media use [30,45]. Participants will also report app-tracked time on Facebook over the past 7 days and their Facebook use habits, including what device(s) they used to access Facebook, what proportion of use was from their smartphone (vs other devices), and the extent to which other people used Facebook on their phone [46].…”