Dataset Information

Developing a crosswalk between the RAND-12 and the health utilities index for multiple sclerosis.

ABSTRACT: BACKGROUND:Researchers studying health-related quality of life (HRQOL) in multiple sclerosis (MS) can choose from many instruments, but findings from studies which use different instruments cannot be easily combined. We aimed to develop a crosswalk that associates scores from the RAND-12 to scores on the Health Utilities Index-Mark III (HUI3) in persons with MS. METHODS:In 2018, participants in the North American Research Committee on Multiple Sclerosis (NARCOMS) registry completed the RAND-12 and the HUI3 to assess HRQOL. We used item-response theory (IRT) and equipercentile linking approaches to develop a crosswalk between instruments. We compared predicted scores for the HUI3 from each crosswalk to observed scores using Pearson correlations, intraclass correlation coefficients (ICCs), and Bland-Altman plots. RESULTS:Of 11,389 invited participants, 7129 (62.6%) responded. Predicted and observed values of the HUI3 from the IRT-linking method were moderately correlated (Pearson r?=?0.76) with good concordance (ICC?=?0.72). However, the Bland-Altman plots suggested biased prediction. Predicted and observed values from the equipercentile linking method were also moderately correlated (Pearson r?=?0.78, ICC?=?0.78). The Bland-Altman plots suggested no bias. CONCLUSION:We developed a crosswalk between the RAND-12 and the HUI3 in the MS population which will facilitate data harmonization efforts.

SUBMITTER: Marrie RA

PROVIDER: S-EPMC7412875 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Developing a crosswalk between the RAND-12 and the health utilities index for multiple sclerosis.

Marrie Ruth Ann RA Dufault Brenden B Tyry Tuula T Cutter Gary R GR Fox Robert J RJ Salter Amber A

Multiple sclerosis (Houndmills, Basingstoke, England) 20190604 9

<h4>Background</h4>Researchers studying health-related quality of life (HRQOL) in multiple sclerosis (MS) can choose from many instruments, but findings from studies which use different instruments cannot be easily combined. We aimed to develop a crosswalk that associates scores from the RAND-12 to scores on the Health Utilities Index-Mark III (HUI3) in persons with MS.<h4>Methods</h4>In 2018, participants in the North American Research Committee on Multiple Sclerosis (NARCOMS) registry complete ...[more]

PMID: 31161917

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Project description:Our inability to reliably predict disease outcomes in multiple sclerosis remains an issue for clinicians and clinical trialists. This study aims to create, from available clinical, genetic and environmental factors; a clinical-environmental-genotypic prognostic index to predict the probability of new relapses and disability worsening. The analyses cohort included prospectively assessed multiple sclerosis cases (N = 253) with 2858 repeated observations measured over 10 years. N = 219 had been diagnosed as relapsing-onset, while N = 34 remained as clinically isolated syndrome by the 10th-year review. Genotype data were available for 199 genetic variants associated with multiple sclerosis risk. Penalized Cox regression models were used to select potential genetic variants and predict risk for relapses and/or worsening of disability. Multivariable Cox regression models with backward elimination were then used to construct clinical-environmental, genetic and clinical-environmental-genotypic prognostic index, respectively. Robust time-course predictions were obtained by Landmarking. To validate our models, Weibull calibration models were used, and the Chi-square statistics, Harrell's C-index and pseudo-R 2 were used to compare models. The predictive performance at diagnosis was evaluated using the Kullback-Leibler and Brier (dynamic) prediction error (reduction) curves. The combined index (clinical-environmental-genotypic) predicted a quadratic time-dynamic disease course in terms of worsening (HR = 2.74, CI: 2.00-3.76; pseudo-R 2=0.64; C-index = 0.76), relapses (HR = 2.16, CI: 1.74-2.68; pseudo-R 2 = 0.91; C-index = 0.85), or both (HR = 3.32, CI: 1.88-5.86; pseudo-R 2 = 0.72; C-index = 0.77). The Kullback-Leibler and Brier curves suggested that for short-term prognosis (≤5 years from diagnosis), the clinical-environmental components of disease were more relevant, whereas the genetic components reduced the prediction errors only in the long-term (≥5 years from diagnosis). The combined components performed slightly better than the individual ones, although their prognostic sensitivities were largely modulated by the clinical-environmental components. We have created a clinical-environmental-genotypic prognostic index using relevant clinical, environmental, and genetic predictors, and obtained robust dynamic predictions for the probability of developing new relapses and worsening of symptoms in multiple sclerosis. Our prognostic index provides reliable information that is relevant for long-term prognostication and may be used as a selection criterion and risk stratification tool for clinical trials. Further work to investigate component interactions is required and to validate the index in independent data sets.

Project description:BackgroundGlobal 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.ObjectiveTo 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.DesignFollowing 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.ParticipantsParticipants (N?=?2025) were 49 % male and 73 % white; mean age was 46 years.Main measuresMental 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 resultsWe 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.ConclusionsVR-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.

Project description:BackgroundThe translated and culturally adapted German version of the Veterans Rand 36 Items Health Survey (VR-36), and its short form, the VR-12 counterpart, were validated in a German sample of orthopedic (n = 399) and psychosomatic (n = 292) inpatient rehabilitation patients.MethodsThe instruments were analyzed regarding their acceptance, distributional properties, validity, responsiveness and ability to discriminate between groups by age, sex and clinically specific groups. Eligible study participants completed the VR-36 (n = 169) and the VR-12 (n = 177). They also completed validated patient-reported outcome measures (PROs) including the Euroqol-5 Dimensions 5 Level (EQ-5D-5L); Depression, Anxiety and Stress Scale (DASS); Hannover Functional Abilities Questionnaire (HFAQ); and CDC Healthy Days. The VR-12 and the VR-36 were compared to the reference instruments MOS Short Form-12 Items Health Survey (SF-12) version 1.0 and MOS Short Form-36 Items Health Survey (SF-36) version 1.0, using percent of completed items, distributional properties, correlation patterns, distribution measures of known groups validity, and effect size measures.ResultsItem non-response varied between 1.8%/1.1% (SFVR-36/RESF-36) and 6.5%/8.6% (GHVR-36/GHSF-36). PCS was normally distributed (Kolmogorov-Smirnov tests: p > 0.05) with means, standard deviations and ranges very similar between SF-36 (37.5 ± 11.7 [13.8-66.1]) and VR-36 (38.5 ± 10.1 [11.7-67.8]), SF-12 (36.9 ± 10.9 [15.5-61.6]) and VR-12 (36.2 ± 11.5 [12.7-59.3]). MCS was not normally distributed with slightly differing means and ranges between the instruments (MCSVR-36: 36.2 ± 14.2 [12.9-66.6], MCSSF-36: 39.0 ± 15.6 [2.0-73.2], MCSVR-12: 37.2 ± 13.8 [8.4-70.2], MCSSF-12: 39.0 ± 12.3 [17.6-65.4]). Construct validity was established by comparing correlation patterns of the MCSVR and PCSVR with measures of physical and mental health. For both PCSVR and MCSVR there were moderate (≥ 0.3) to high (≥ 0.5) correlations with convergent (PCSVR: 0.55-0.76, MCSVR: 0.60-0.78) and small correlations (< 0.1) with divergent (PCSVR: < 0.12, MCSVR: < 0.16) self-report measures. Known-groups validity was demonstrated for both VR-12 and VR-36 (MCS and PCS) via comparisons of distribution parameters with significant higher mean PCS and MCS scores in both VR instruments found in younger patients with fewer sick days in the last year and a shorter duration of rehabilitation.ConclusionsThe psychometric analysis confirmed that the German VR is a valid and reliable instrument for use in orthopedic and psychosomatic rehabilitation. Yet further research is needed to evaluate its usefulness in other populations.

Dataset Information

Developing a crosswalk between the RAND-12 and the health utilities index for multiple sclerosis.

Publications

Developing a crosswalk between the RAND-12 and the health utilities index for multiple sclerosis.

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