How Are Age, Gender, and Country Differences Associated With PROMIS Physical Function, Upper Extremity, and Pain Interference Scores?

BACKGROUND: The interpretation of patient-reported outcomes requires appropriate comparison data. Currently, no patient-specific reference data exist for the Patient-Reported Outcome Measurement Information System (PROMIS) Physical Function (PF), Upper Extremity (UE), and Pain Interference (PI) scales for individuals 50 years and older. QUESTIONS/PURPOSES: (1) Can all PROMIS PF, UE, and PI items be used for valid cross-country comparisons in these domains among the United States, the United Kingdom, and Germany? (2) How are age, gender, and country related to PROMIS PF, PROMIS UE, and PROMIS PI scores? (3) What is the relationship of age, gender, and country across individuals with PROMIS PF, PROMIS UE, and PROMIS PI scores ranging from very low to very high? METHODS: We conducted telephone interviews to collect custom PROMIS PF (22 items), UE (eight items), and PI (eight items) short forms, as well as sociodemographic data (age, gender, work status, and education level), with participants randomly selected from the general population older than 50 years in the United States (n = 900), United Kingdom (n = 905), and Germany (n = 921). We focused on these individuals because of their higher prevalence of surgeries and lower physical functioning. Although response rates varied across countries (14% for the United Kingdom, 22% for Germany, and 12% for the United States), we used existing normative data to ensure demographic alignment with the overall populations of these countries. This helped mitigate potential nonresponder bias and enhance the representativeness and validity of our findings. We investigated differential item functioning to determine whether all items can be used for valid crosscultural comparisons. To answer our second research question, we compared age groups, gender, and countries using median regressions. Using imputation of plausible values and quantile regression, we modeled age-, gender-, and country-specific distributions of PROMIS scores to obtain patient-specific reference values and answer our third research question. RESULTS: All items from the PROMIS PF, UE, and PI measures were valid for across-country comparisons. We found clinically meaningful associations of age, gender, and country with PROMIS PF, UE, and PI scores. With age, PROMIS PF scores decreased (age ß Median = -0.35 [95% CI -0.40 to -0.31]), and PROMIS UE scores followed a similar trend (age ß Median = -0.38 [95% CI -0.45 to -0.32]). This means that a 10-year increase in age corresponded to a decline in approximately 3.5 points for the PROMIS PF score-a value that is approximately the minimum clinically important difference (MCID). Concurrently, we observed a modest increase in PROMIS PI scores with age, reaching half the MCID after 20 years. Women in all countries scored higher than men on the PROMIS PI and 1 MCID lower on the PROMIS PF and UE. Additionally, there were higher T-scores for the United States than for the United Kingdom across all domains. The difference in scores ranged from 1.21 points for the PROMIS PF to a more pronounced 3.83 points for the PROMIS UE. Participants from the United States exhibited up to half an MCID lower T-scores than their German counterparts for the PROMIS PF and PROMIS PI. In individuals with high levels of physical function, with each 10-year increase in age, there could be a decrease of up to 4 points in PROMIS PF scores. Across all levels of upper extremity function, women reported lower PROMIS UE scores than men by an average of 5 points. CONCLUSION: Our study provides age-, gender-, and country-specific reference values for PROMIS PF, UE, and PI scores, which can be used by clinicians, researchers, and healthcare policymakers to better interpret patient-reported outcomes and provide more personalized care. These findings are particularly relevant for those collecting patient-reported outcomes in their clinical routine and researchers conducting multinational studies. We provide an internet application ( www.common-metrics.org/PROMIS_PF_and_PI_Reference_scores.php ) for user-friendly accessibility in order to perform age, gender, and country conversions of PROMIS scores. Population reference values can also serve as comparators to data collected with other PROMIS short forms or computerized adaptive tests. LEVEL OF EVIDENCE: Level II, diagnostic study.

Clinical Validity of the PROMIS Healthcare Engagement 8-Item Short Form.

BACKGROUND: Healthcare engagement is a key measurement target for value-based healthcare, but a reliable and valid patient-reported measure has not yet been widely adopted. OBJECTIVE: To assess the validity of a newly developed patient-reported measure of healthcare engagement, the 8-item PROMIS Healthcare Engagement (PHE-8a). DESIGN: Prospective cohort study of the association between healthcare engagement and quality of care over 1 year. We fit mixed effects models of quality indicators as a function of engagement scores, adjusting for age, race/ethnicity, rural residence, and risk scores. PARTICIPANTS: National stratified random sample of 9552 Veterans receiving Veterans Health Administration care for chronic conditions (hypertension, diabetes) or mental health conditions (depression, post-traumatic stress disorder). MAIN MEASURES: Patient experience: Consumer Assessment of Health Plans and Systems communication and self-management support composites; no-show rates for primary care and mental health appointments; use of patient portal My HealtheVet; and Healthcare Effectiveness Data and Information Set electronic quality measures: HbA1c poor control, controlling high blood pressure, and hyperlipidemia therapy adherence. KEY RESULTS: Higher engagement scores were associated with better healthcare quality across all outcomes, with each 5-point increase (1/2 standard deviation) in engagement scores associated with statistically significant and clinically meaningful gains in quality. Across the continuum of low to high engagement scores, we observed a concomitant reduction in primary care no-show rates of 37% and 24% for mental health clinics; an increased likelihood of My HealtheVet use of 15.4%; and a decreased likelihood of poor diabetes control of 44%. CONCLUSIONS: The PHE-8a is a brief, reliable, and valid patient-reported measure of healthcare engagement. These results confirm previously untested hypotheses that patient engagement can promote healthcare quality.

Does Scoring Method Impact Estimation of Significant Individual Changes Assessed by Patient-Reported Outcome Measures? Comparing Classical Test Theory Versus Item Response Theory.

OBJECTIVES: This study aimed to examine the ability of classical test theory (CTT) and item response theory (IRT) scores assessed by Patient-Reported Outcomes Measurement Information System® (PROMIS®) measures to identify significant individual changes in the setting of clinical studies, using both simulated and empirical data. METHODS: We used simulated data to compare the estimation of significant individual changes between CTT and IRT scores across different conditions and a clinical trial data set to verify the simulation results. We calculated reliable change indexes to estimate significant individual changes. RESULTS: For small true change, IRT scores showed a slightly higher rate of classifying change groups than CTT scores and were comparable with CTT scores for a shorter test length. Additionally, IRT scores were found to have a prominent advantage in the classification rates of change groups for medium to high true change over CTT scores. Such an advantage became prominent in a longer test length. The empirical data analysis results using an anchor-based approach further supported the above findings that IRT scores can more accurately classify participants into change groups than CTT scores. CONCLUSIONS: Given that IRT scores perform better, or at least comparably, in most conditions, we recommend using IRT scores to estimate significant individual changes and identify responders to treatment. This study provides evidence-based guidance in detecting individual changes based on CTT and IRT scores under various measurement conditions and leads to recommendations for identifying responders to treatment for participants in clinical trials.

Using bookmarking methods with orthopedic clinicians and patients with fractures produces score interpretation labels for patient-reported outcome measures.

OBJECTIVE: The objective of this study was to determine the patient-reported outcome measure (PROM) score ranges associated with descriptive labels (i.e., within normal limits, mild, moderate, severe) by using bookmarking methods with orthopedic clinicians and patients who have experienced a bone fracture. STUDY DESIGN AND SETTING: We created vignettes comprised of six items and responses from the Patient-Reported Outcomes Measurement Information System (PROMIS) Upper Extremity Function, Physical Function, and Pain Interference item banks reflecting different levels of severity. Two groups of patients with fractures (n = 11) and two groups of orthopedic clinicians (n = 16) reviewed the vignettes and assigned descriptive labels independently and then discussed as a group until reaching consensus via a videoconference platform. RESULTS: PROMIS Physical Function and Pain Interference thresholds (T = 50, 40, 25/30 and T = 50/55, 60, 65/70, respectively) for patients with bone fractures were consistent with the results from other patient populations. Upper Extremity thresholds were about 10 points (1 SD) more severe (T = 40, 30, 25/20) compared to the other measures. Patient and clinician perspectives were similar. CONCLUSION: Bookmarking methods generated meaningful score thresholds for PROMIS measures. These thresholds between severity categories varied by domain. Threshold values for severity represent important supplemental information to interpret PROMIS scores clinically.

Do You Recall?: Results From a Within-Person Recall Study of the Patient-Reported Outcomes Measurement Information System (PROMIS) Short Form v2.0 - Physical Function 8c.

OBJECTIVES: This study aimed to determine whether responses to Patient-Reported Outcomes Measurement Information System Short Form v2.0 - Physical Function 8c (PROMIS PF8c) items differed when the use of a 7-day recall period was compared with no specified recall period. METHODS: Using a within-subject design, we surveyed 1810 individuals from the US general population, administering PROMIS PF8c at survey beginning and end. The order of measure presentation was randomly assigned. We calculated recall difference scores (RDSs) as no recall score minus 7-day recall score using both item response theory-based T scores and raw summed scores. We examined the distribution and created Bland-Altman plots for both RDSTscore and RDSRaw. We also calculated correlations between no recall versus 7-day recall T score and raw scores. Finally, we determined whether differences in no recall versus 7-day recall scores were associated with patient-reported PF. RESULTS: RDSTscore and RDSRaw had means (root mean square differences) of 0.00 (5.43) and -0.04 (3.79), respectively. The vast majority (%) of RDSTscore and RDSRaw values fell between the Bland-Altman limits of agreement (-10.65 to 10.66 and -7.46 to 7.38, respectively). Pearson's correlations between no recall and 7-day recall for T scores and raw scores were 0.88 and 0.87, respectively. Effect sizes for mean RDSTscore and RDSRaw compared across level of Eastern Oncology Cooperative Group performance status, patient global impression of PF severity, and single PF items were near 0. CONCLUSIONS: We did not find any significant recall period effect on PF8c responses. Therefore, we recommend the use of the PROMIS physical function standard, with no specified recall time period.

Linking the RCADS-25 to the PROMIS® pediatric item banks Anxiety and Depressive Symptoms in a general Dutch population sample.

PURPOSE: This study aims to link scores of the Revised Child Anxiety and Depression Scale short version (RCADS-25) to the metric of the PROMIS pediatric item banks Anxiety and Depressive Symptoms in a general Dutch population sample. METHODS: The RCADS-25 and PROMIS pediatric item banks Anxiety and Depressive Symptoms were administered online to 2,893 Dutch children and adolescents aged 8-18. Assumptions for linking methods were checked. Linking was achieved by using three item response theory (IRT) and two equipercentile methods. For each method, the observed PROMIS metric scores were compared to the ones predicted from the RCADS-25 subscale scores using correlations, mean and SD of differences, and RMSD. RESULTS: The assumptions for IRT-based and equipercentile linking were met. The IRT-based method using separate calibration with Stocking-Lord constants was considered the optimal choice for linking both RCADS-25 subscales to the PROMIS metric. Based on this Stocking-Lord approach, we created item parameters for RCADS-25 items and two crosswalk tables. CONCLUSION: The RCADS-25 item parameters and crosswalk tables presented in this study are sufficiently valid. Researchers can use these products to translate the RCADS-25 anxiety and depression subscales scores to the PROMIS metric in order to ensure comparability with the previous research.

Interindividual variation contributes to differential PCB 126 induced gene expression in primary breast epithelial cells and tissues.

PCB 126 is a pervasive, dioxin-like chemical pollutant which can activate the aryl hydrocarbon receptor (AhR). Despite being banned from the market, PCB 126 can be detected in breast milk to this day. The extent to which interindividual variation impacts the adverse responses to this chemical in the breast tissue remains unclear. This study aimed to investigate the impact of 3 nM PCB 126 on gene expression in a panel of genetically diverse benign human breast epithelial cell (HBEC) cultures and patient derived breast tissues. Six patient derived HBEC cultures were treated with 3 nM PCB 126. RNAseq was used to interrogate the impact of exposure on differential gene expression. Gene expression changes from the top critical pathways were confirmed via qRT-PCR in a larger panel of benign patient derived HBEC cultures, as well as in patient-derived breast tissue explant cultures. RNAseq analysis of HBEC cultures revealed a signature of 144 genes significantly altered by 3 nM PCB 126 treatment. Confirmation of 8 targets using a panel of 12 HBEC cultures and commercially available breast cell lines demonstrated that while the induction of canonical downstream target gene, CYP1A1, was consistent across our primary HBECs, other genes including AREG, S100A8, IL1A, IL1B, MMP7, and CCL28 exhibited significant variability across individuals. The dependence on the activity of the aryl hydrocarbon receptor was confirmed using inhibitors. PCB 126 can induce significant and consistent changes in gene expression associated with xenobiotic metabolism in benign breast epithelial cells. Although the induction of most genes was reliant on the AhR, significant variability was noted between genes and individuals. These data suggest that there is a bifurcation of the pathway following AhR activation that contributes to the variation in interindividual responses.

Psychometric evaluation of a patient-reported item bank for healthcare engagement.

PURPOSE: Healthcare engagement is a core measurement target for efforts to improve healthcare systems. This construct is broadly defined as the extent to which healthcare services represent collaborative partnerships with patients. Previous qualitative work operationalized healthcare engagement as generalized self-efficacy in four related subdomains: self-management, collaborative communication, health information use, and healthcare navigation. Building on this work, our objective was to establish a healthcare engagement instrument that is sufficiently unidimensional to yield a single score. METHOD: We conducted cognitive interviews followed by a nation-wide mail survey of US Veteran Administration (VA) healthcare users. Data were collected on 49 candidate healthcare engagement items, as well as measures of self-efficacy for managing symptoms, provider communication, and perceived access. Items were subjected to exploratory bifactor, statistical learning, and IRT analyses. RESULTS: Cognitive interviews were completed by 56 patients and 9552 VA healthcare users with chronic conditions completed the mail survey. Participants were mostly white and male but with sizable minority participation. Psychometric analyses and content considerations reduced the item pool to 23 items, which demonstrated a strong general factor (OmegaH of .89). IRT analyses revealed a high level of reliability across the trait range and little DIF across groups. Most health information use items were removed during analyses, suggesting a more independent role for this domain. CONCLUSION: We provide quantitative evidence for a relatively unidimensional measure of healthcare engagement. Despite developed with VA healthcare users, the measure is intended for general use. Future work includes short-form development and validation with other patient groups.

Can the Knee Outcome and Osteoarthritis Score (KOOS) Function Subscale Be Linked to the PROMIS Physical Function to Crosswalk Equivalent Scores?

BACKGROUND: An increased focus on patient-reported outcome measures (PROMs) has led to a proliferation of these measures in orthopaedic surgery. Mandating a single PROM in clinical and research orthopaedics is not feasible given the breadth of data already collected with older measures and the emergence of psychometrically superior measures. Creating crosswalk tables for scores between measures allows providers to maintain control of measure choice. Furthermore, crosswalk tables permit providers to compare scores collected with older outcome measures with newly collected ones. Given the widespread use of the newer Patient-reported Outcome Measure Information System Physical Function (PROMIS PF) and the established Knee Outcome and Osteoarthritis Score (KOOS), it would be clinically useful to link these two measures. QUESTION/PURPOSE: Can the KOOS Function in Activities of Daily Living (ADL) subscale be robustly linked to the PROMIS PF to create a crosswalk table of equivalent scores that accurately reflects a patient's reported physical function level on both scales? METHODS: We sought to establish a common standardized metric for collected responses to the PROMIS PF and the KOOS ADL to develop equations for converting a PROMIS PF score to a score for the KOOS-ADL subscale and vice versa. To do this, we performed a retrospective, observational study at two academic medical centers and two community hospitals in an urban and suburban healthcare system. Patients 18 years and older who underwent TKA were identified. Between January 2017 and July 2020, we treated 8165 patients with a TKA, 93% of whom had a diagnosis of primary osteoarthritis. Of those, we considered patients who had completed a full KOOS and PROMIS PF 10a on the same date as potentially eligible. Twenty-one percent (1708 of 8165) of patients were excluded because no PROMs were collected at any point, and another 67% (5454 of 8165) were excluded because they completed only one of the required PROMs, leaving 12% (1003 of 8165) for analysis here. PROMs were collected each time they visited the health system before and after their TKAs. Physical function was measured by the PROMIS PF version 1.0 SF 10a and KOOS ADL scale. Analyses to accurately create a crosswalk of equivalent scores between the measures were performed using the equipercentile linking method with both unsmoothed and log linear smoothed score distributions. RESULTS: Crosswalks were created, and adequate validation results supported their validity; we also created tables to allow clinicians and clinician scientists to convert individual patients' scores easily. The mean difference between the observed PROMIS PF scores and the scores converted by the crosswalk from the KOOS-ADL scores was -0.08 ± 4.82. A sensitivity analysis was conducted, confirming the effectiveness of these crosswalks to link the scores of two measures from patients both before and after surgery. CONCLUSION: The PROMIS PF 10a can be robustly linked to the KOOS ADL measure. The developed crosswalk table can be used to convert PROMIS PF scores from KOOS ADL and vice versa. CLINICAL RELEVANCE: The creation of a crosswalk table between the KOOS Function in ADL subscale and PROMIS PF allows clinicians and researchers to easily convert scores between the measures, thus permitting greater choice in PROM selection while preserving comparability between patient cohorts and PROM data collected from older outcome measures. Creating a crosswalk, or concordance table, between the two scales will facilitate this comparison, especially when pooling data for meta-analyses.

The use of patient-derived breast tissue explants to study macrophage polarization and the effects of environmental chemical exposure.

Ex vivo mammary explant systems are an excellent model to study interactions between epithelium and stromal cell types because they contain physiologically relevant heterotypic interactions in the background of genetically diverse patients. The intact human mammary tissue, termed patient-derived explant (PDE), can be used to investigate cellular responses to a wide variety of external stimuli in situ. For this study, we examined the impact of cytokines or environmental chemicals on macrophage phenotypes. We demonstrate that we can polarize macrophages within human breast tissue PDEs toward M1 or M2 through the addition of interferon-γ (IFNγ) + lipopolysaccharide (LPS) or interleukin (IL)-4 + IL-13, respectively. Elevated expression levels of M(IFNγ + LPS) markers (HLADRA and CXCL10) or M(IL-4 + IL-13) markers (CD209 and CCL18) were observed in cytokine-treated tissues. We also examined the impact of the endocrine-disrupting chemical, benzophenone-3, on PDEs and measured significant, yet varying effects on macrophage polarization. Furthermore, a subset of the PDEs respond to IL-4 + IL-13 through downregulation of E-cadherin and upregulation of vimentin which is reminiscent of epithelial-to-mesenchymal transition (EMT) changes. Finally, we were able to show immortalized nonmalignant breast epithelial cells can exhibit EMT characteristics when exposed to growth factors secreted by M(IL-4 + IL-13) macrophages. Taken together, the PDE model system is an outstanding preclinical model to study early tissue-resident immune responses and effects on epithelial and stromal responses to stimuli found both endogenously in the breast and exogenously as a result of exposures.

Psychometric Data Linking Across HIV and Substance Use Cohorts.

Psychometric data linking of psychological and behavioral questionnaires can facilitate the harmonization of data across HIV and substance use cohorts. Using data from the Collaborating Consortium of Cohorts Producing NIDA Opportunities (C3PNO), we demonstrate how to capitalize on previous linking work with a common linked depression metric across multiple questionnaires. Cohorts were young men who have sex with men (MSM), substance-using MSM, HIV/HCV cocaine users, and HIV-positive patients. We tested for differential item functioning (DIF) by comparing C3PNO cohort data with general population data. We also fit a mixed-effects model for depression, entering HIV-status and recent opioid/heroin use as fixed effects and cohort as a random intercept. Our results suggest a minimal level of DIF between the C3PNO cohorts and general population samples. After linking, descriptive statistics show a wide range of depression score means across cohorts. Our model confirmed an expected positive relationship between substance use and depression, though contrary to expectations, no significant association with HIV status. The study reveals the likely role of cohort differences, associated patient characteristics, study designs, and administration settings.

A comparison of computer adaptive tests (CATs) and short forms in terms of accuracy and number of items administrated using PROMIS profile.

PURPOSE: In the Patient-Reported Outcomes Measurement Information System (PROMIS), seven domains (Physical Function, Anxiety, Depression, Fatigue, Sleep Disturbance, Social Function, and Pain Interference) are packaged together as profiles. Each of these domains can also be assessed using computer adaptive tests (CATs) or short forms (SFs) of varying length (e.g., 4, 6, and 8 items). We compared the accuracy and number of items administrated of CAT versus each SF. METHODS: PROMIS instruments are scored using item response theory (IRT) with graded response model and reported as T scores (mean = 50, SD = 10). We simulated 10,000 subjects from the normal distribution with mean 60 for symptom scales and 40 for function scales, and standard deviation 10 in each domain. We considered a subject's score to be accurate when the standard error (SE) was less than 3.0. We recorded range of accurate scores (accurate range) and the number of items administrated. RESULTS: The average number of items administrated in CAT was 4.7 across all domains. The accurate range was wider for CAT compared to all SFs in each domain. CAT was notably better at extending the accurate range into very poor health for Fatigue, Physical Function, and Pain Interference. Most SFs provided reasonably wide accurate range. CONCLUSIONS: Relative to SFs, CATs provided the widest accurate range, with slightly more items than SF4 and less than SF6 and SF8. Most SFs, especially longer ones, provided reasonably wide accurate range.

Assessing meaning & purpose in life: development and validation of an item bank and short forms for the NIH PROMIS®.

PURPOSE: A sense of meaning and purpose is important for people living with acute and chronic illness. It can buffer the effects of stress and facilitate adaptive coping. As part of the Patient-Reported Outcomes Measurement Information System (PROMIS), we developed and validated an item response theory (IRT)-based measure of meaning and purpose in life. METHODS: Informed by a literature review and patient and content-expert input, we wrote 52 items to assess meaning and purpose and administered them to a general population sample (n = 1000) along with the Meaning in Life Questionnaire-Presence of Meaning Subscale (MLQ-Presence) and the Life Engagement Test (LET). We split the sample in half for exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA). IRT analyses included assessments of differential item functioning (DIF). RESULTS: Participants had a mean age of 47.8 years and 50.3% were male. EFA revealed one dominant factor and CFA yielded a good fitting model for a 37-item bank (CFI = 0.962, TLI = 0.960, RMSEA = 0.085). All items were free of sex, age, education, and race DIF. Internal consistency reliability estimates ranged from α = 0.90 (4-item short form) to α = 0.98 (37-item bank). The 8-item Meaning and Purpose short form was correlated with the MLQ-Presence (r = 0.89), the LET (r = 0.79), and the full PROMIS Meaning and Purpose item bank (r = 0.98). CONCLUSIONS: The PROMIS Meaning and Purpose measures demonstrated sufficient unidimensionality and displayed good internal consistency, model fit, and convergent validity. Further psychometric testing of the PROMIS Meaning and Purpose item bank and short forms in people with chronic diseases will help evaluate the generalizability of this new tool.

Establishing a common metric for self-reported anxiety in patients with prostate cancer: Linking the Memorial Anxiety Scale for Prostate Cancer with PROMIS Anxiety.

BACKGROUND: Anxiety is a common patient concern and clinical endpoint in prostate cancer outcomes research. It is measured using different self-report instruments that are not directly comparable, thereby making clinical trials, clinical performance measurement, and comparative effectiveness research challenging when anxiety is the outcome of interest. The objective of the current study was to enable a common reporting metric of anxiety so that scores on commonly used anxiety measures could be converted into Patient-Reported Outcomes Measurement Information System (PROMIS) scores for ease of application, interpretation, and comparability. METHODS: Using an internet health panel, a total of 806 men with clinically localized prostate cancer completed items from the National Institutes of Health PROMIS Anxiety Short Form (version 7a) and the 18-item Memorial Anxiety Scale for Prostate Cancer (MAX-PC). A common metric was created using analyses based on item response theory, producing score crosswalk tables. The linking relationships were evaluated by resampling small subsets and estimating confidence intervals for the differences between the observed and linked PROMIS scores. RESULTS: Results of factor analysis and item response theory model fit supported the hypothesis that both scales measure essentially the same concept. Therefore, crosswalk tables appear to be justified and increasingly robust with increasing sample sizes. CONCLUSIONS: MAX-PC Anxiety results can be expressed on the PROMIS Anxiety metric for the purposes of clinical performance measurement, clinical trial outcomes, comparative effectiveness research, and other efforts to compare anxiety results across studies that use any one of these measures.

Calibration and initial validation of a general self-efficacy item bank and short form for the NIH PROMIS®.

AIMS: General self-efficacy is associated with adaptive coping and positive health outcomes. The Patient-Reported Outcomes Measurement Information System (PROMIS®) has developed self-efficacy item banks for managing chronic conditions, but lacks a general self-efficacy measure. We sought to refine and validate an item-response theory (IRT)-based measure of general self-efficacy for PROMIS®. METHODS: Ten items were modified from the NIH Toolbox® Self-Efficacy Item Bank by creating "confidence" response options, and administered to a general population sample (n = 1000) with the Toolbox® Self-Efficacy Item Bank, Life Orientation Test-Revised (LOT-R), and Generalized Expectancy for Success Scale (GESS). We split the sample in half for exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA). IRT analyses included evaluation of differential item functioning (DIF). RESULTS: Participants had a mean age of 47.8 years and 50.3% were male. EFA showed all items loaded onto one dominant factor and CFA yielded a good fitting model for a general self-efficacy bank with confidence response options (CFI = 0.987, TLI = 0.984, RMSEA = 0.090). Items showed no evidence of DIF by gender, age, education, or race. Internal consistency reliability was α = .94 and .88 for a new 10-item general self-efficacy bank and 4-item short form, respectively. The new bank was correlated with the LOT-R (r = .58), the GESS (r = .55), and the Toolbox® Self-Efficacy Item Bank (r = .87). CONCLUSIONS: The PROMIS® General Self-Efficacy measure demonstrated sufficient unidimensionality and displayed good internal consistency reliability, model fit, and convergent validity. Further psychometric testing of the PROMIS® General Self-Efficacy Item Bank and Short Form can evaluate its utility in people with chronic health conditions.

Physical function metric over measure: An illustration with the Patient-Reported Outcomes Measurement Information System (PROMIS) and the Functional Assessment of Cancer Therapy (FACT).

BACKGROUND: Measuring patient-reported outcomes (PROs) is becoming an integral component of quality improvement initiatives, clinical care, and research studies in cancer, including comparative effectiveness research. However, the number of PROs limits comparability across studies. Herein, the authors attempted to link the Functional Assessment of Cancer Therapy-General Physical Well-Being (FACT-G PWB) subscale with the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function (PF) calibrated item bank. The also sought to augment a subset of the conceptually most similar FACT-G PWB items with PROMIS PF items to improve the linking. METHODS: Baseline data from 5506 participants in the Measuring Your Health (MY-Health) study were used to identify the optimal items for linking FACT-G PWB with PROMIS PF. A mixed methods approach identified the optimal items for creating the 5-item FACT/PROMIS-PF5 scale. Both the linked and augmented relationships were cross-validated using the follow-up MY-Health data. RESULTS: A 5-item FACT-G PWB item subset was found to be optimal for linking with PROMIS PF. In addition, a 2-item subset, including only items that were conceptually very similar to the PROMIS item bank content, were augmented with 3 PROMIS PF items. This new FACT/PROMIS-PF5 provided superior score recovery. CONCLUSIONS: The PROMIS PF metric allows for the evaluation of the extent to which similar questionnaires can be linked and therefore expressed on the same metric. These results allow for the aggregation of existing data and provide an optimal measure for future studies wishing to use the FACT yet also report on the PROMIS PF metric. Cancer 2018;124:153-60. © 2017 American Cancer Society.

PROMIS®-29 v2.0 profile physical and mental health summary scores.

PURPOSE: The PROMIS-29 v2.0 profile assesses pain intensity using a single 0-10 numeric rating item and seven health domains (physical function, fatigue, pain interference, depressive symptoms, anxiety, ability to participate in social roles and activities, and sleep disturbance) using four items per domain. This paper describes the development of physical and mental health summary scores for the PROMIS-29 v2.0. METHOD: We conducted factor analyses of PROMIS-29 scales on data collected from two internet panels (n = 3000 and 2000). RESULTS: Confirmatory factor analyses provided support for a physical health factor defined by physical function, pain (interference and intensity), and ability to participate in social roles and activities, and a mental health factor defined primarily by emotional distress (anxiety and depressive symptoms). Reliabilities for these two summary scores were 0.98 (physical health) and 0.97 (mental health). Correlations of the PROMIS-29 v2.0 physical and mental health summary scores with chronic conditions and other health-related quality of life measures were consistent with a priori hypotheses. CONCLUSIONS: This study develops and provides preliminary evidence supporting the reliability and validity of PROMIS-29 v2.0 physical and mental health summary scores that can be used in future studies to assess impacts of health care interventions and track changes in health over time. Further evaluation of these and alternative summary measures is recommended.

Factors Affecting Quality of Life at Different Intervals After Treatment of Localized Prostate Cancer: Unique Influence of Treatment Decision Making Satisfaction, Personality and Sexual Functioning.

PURPOSE: Using patient reported outcomes measures we identified the most informative set of factors associated with quality of life in a large sample of men treated for localized prostate cancer. MATERIALS AND METHODS: We examined relationships with quality of life using FACIT (Functional Assessment of Chronic Illness Therapy). We also hypothesized variables in a sample of men diagnosed with localized prostate cancer who represented different time points since treatment, including less than 12 months in 70, 1 to 3 years in 344, greater than 3 to 5 years in 291 and greater than 5 years in 97. Correlative measures included subscales of MAX-PC (Memorial Anxiety Scale for Prostate Cancer), short forms of PROMIS® and SOMS (Surgical Outcomes Measurement System), TDM-SATS (Treatment Decision-Making Satisfaction Scale) and subscales of the BFI (Big Five Inventory) of personality. RESULTS: Quality of life was significantly associated with hypothesized variables across different time cohorts. In regression models several factors accounted for most of the variability in quality of life scores depending on time since treatment, including 47%, 22%, 29% and 27% at less than 12 months, 1 to 3 years, greater than 3 to 5 years and greater than 5 years, respectively. Upon examining the unique contribution of these variables, treatment decision making satisfaction was the only variable to have a significant and unique contribution to quality of life across all 4 time cohorts (standardized coefficients 0.33, 0.27, 0.31 and 0.49, respectively, p <0.01). In the cohort with 1 to 3 years since treatment erectile function and neurotic personality style also had unique associations with quality of life (standardized coefficients 0.25 and -0.20, respectively). CONCLUSIONS: When considering the short-term and the longer term quality of life of a man after treatment for localized prostate cancer, our findings highlight the importance of treatment decision making satisfaction, erectile function and personality.

Establishing a Common Metric for Physical Function: Linking the HAQ-DI and SF-36 PF Subscale to PROMIS(®) Physical Function.

BACKGROUND: Physical function (PF) is a common health concept measured in clinical trials and clinical care. It is measured with different instruments that are not directly comparable, making comparative effectiveness research (CER) challenging when PF is the outcome of interest. OBJECTIVE: Our goal was to establish a common reporting metric, so that scores on commonly used physical function measures can be converted into PROMIS scores. DESIGN: Following a single-sample linking design, all participants completed items from the NIH Patient Reported Outcomes Measurement Information System (PROMIS®) Physical Function (PROMIS PF) item bank and at least one other commonly used "legacy" measure: the Health Assessment Questionnaire (HAQ) or the Short Form-36 physical function ten-item PF scale (SF-36 PF). A common metric was created using analyses based on item response theory (IRT), producing score cross-walk tables. PARTICIPANTS: Participants (N = 733) were part of an internet panel, many of whom reported one or more chronic health conditions. MAIN MEASURES: PROMIS PF, SF-36 PF, and the HAQ-Disability Index (HAQ-DI). RESULTS: Our results supported the hypothesis that all three scales measure essentially the same concept. Cross-walk tables for use in CER are therefore justified. CONCLUSIONS: HAQ-DI and SF-36 PF results can be expressed on the PROMIS PF metric for the purposes of CER and other efforts to compare PF results across studies that utilize any one of these three measures. Clinicians seeking to incorporate PROs into their clinics can collect patient data on any one of these three instruments and estimate the equivalent on the other two.

Linking Physical and Mental Health Summary Scores from the Veterans RAND 12-Item Health Survey (VR-12) to the PROMIS(®) Global Health Scale.

BACKGROUND: Global health measures represent an attractive option for researchers and clinicians seeking a brief snapshot of a patient's overall perspective on his or her health. Because scores on different global health measures are not comparable, comparative effectiveness research (CER) is challenging. OBJECTIVE: To establish a common reporting metric so that the physical and mental health scores on the Veterans RAND 12-Item Health Survey (VR-12 (©) ) can be converted into scores on the corresponding Patient Reported Outcomes Measurement Information System (PROMIS(®)) Global Health scores. DESIGN: Following a single-sample linking design, participants from an Internet panel completed items from the PROMIS Global Health and VR-12 Health Survey. A common metric was created using analyses based on item response theory (IRT), producing score cross-walk tables for the mental and physical health components of each measure. The linking relationships were evaluated by calculating the standard deviation of differences between the observed and linked PROMIS scores and estimating confidence intervals by sample size. PARTICIPANTS: Participants (N = 2025) were 49 % male and 73 % white; mean age was 46 years. MAIN MEASURES: Mental and physical health subscales of the PROMIS Global Health and the VR-12. The mean VR-12 physical component and mental component scores were 45.2 and 46.6, respectively; the mean PROMIS physical and mental health scores were 48.3 and 48.5, respectively. KEY RESULTS: We found evidence that the combined set of VR-12 and PROMIS items were relatively unidimensional and that we could proceed with linking. Linking worked better between the physical health than mental health scores using VR-12 item responses (vs. linking based on algorithmic scores). For each of the cross-walks, users can minimize the impact of linking error with modest increases in sample sizes. CONCLUSIONS: VR-12 scores can be expressed on the PROMIS Global Health metric to facilitate the evaluation of treatment, including CER. Extending these results to other common measures of global health is encouraged.