Item Response Model Adaptation for Analyzing Data from Different Versions of Parkinson’s Disease Rating Scales

Gottipati, Gopichand; Berges, Aliénor; Yang, Shuying; Chen, Chao; Karlsson, Mats O.; Plan, Elodie L.

doi:10.1007/s11095-019-2668-6

Cited by 15 publications

(32 citation statements)

References 23 publications

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“…14 The use of IRT as an approach to examine clinimetric properties of the MDS-UPDRS is recent, with methodological variability in the IRT models used, characteristics of PD samples studied, and portions of the scale used in the analyses. Most of these studies use longitudinal data from the Parkinson's Progression Marker initiative, [15][16][17][18] a cohort of early-stage PD patients who do not require treatment at baseline and are followed longitudinally. The majority of these studies applied IRT methods to examine longitudinal change of either total scale scores or unique combinations of different items of the MDS-UPDRS.…”

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confidence: 99%

Item Response Theory Analysis of the MDS‐UPDRS Motor Examination: Tremor vs. Nontremor Items

et al. 2020

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Background In PD, tremor severity behaves differently from other core motor features. However, the most commonly used assessment of overall motor severity, total MDS‐UPDRS Motor Examination (Part 3) score, does not account for this distinction. Objectives To investigate the Motor Examination (Part 3) using Item Response Theory approaches focusing on sample‐independent strategies that assess how well items measure latent models of PD motor severity. Methods Data from 6,298 PD patients were analyzed with graded response model Item Response Theory approaches involving two analyses all 33 Part 3 items versus the 10 tremor items and 23 bradykinesia, rigidity, gait, and posture items considered separately. The strength of relationship between items and the latent measure of parkinsonian motor severity (discrimination parameter) and calculated thresholds (location parameters) were assessed using the mirt program implemented in R (R Foundation for Statistical Computing, Vienna, Austria). Results Analyzing all Part 3 items together, nontremor items demonstrated good discrimination parameters (mean = 1.83 ± 0.37) and range of thresholds (–1.73 to +4.42), but tremor items had poor discrimination (mean = 0.52 ± 0.76) and thresholds (–0.69 to 14.29). Segregating nontremor from tremor items in two independent analyses provided markedly improved discrimination and location parameters for both. Conclusions MDS‐UPDRS Part 3 tremor and nontremor items have very different relations to the construct of PD severity. Strongly improved clinimetric properties for Part 3 are obtained when tremor and nontremor items are considered separately. We suggest that evaluating PD motor severity, as an operationalized summary measure, is best attained through separate analyses with tremor and nontremor motor scores. © 2020 International Parkinson and Movement Disorder Society

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Item Response Theory Analysis of the MDS‐UPDRS Motor Examination: Tremor vs. Nontremor Items

et al. 2020

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“…By extension, it may be possible to reduce patient burden by asking each patient to take only a stratified partial test. Other potential applications of this approach include bridging between different versions of an evolving instrument for meta-analysis or cross-study comparison, 28 and translating clinical trial results to patient outcome expectations. These areas require extensive further research and experience building by the clinical research community.…”

Section: Discussionmentioning

confidence: 99%

“…Instead of relying on a single composite score of the test, it defines mathematical links for individual items in the instrument to directly estimate a patient's disease severity that the very instrument is designed to measure. For its improved utilization of the data at the item level, IRT has been applied in the research of several neurological diseases such as Parkinson's disease 12,22,28 , Alzheimer's disease 9 , multiple sclerosis 10 and schizophrenia 11 . Remarkably, the methodology have shown promise to significantly reduce the size of drug trials 9,22 .…”

Section: Introductionmentioning

confidence: 99%

Modelling item scores of Unified Parkinson's Disease Rating Scale for greater trial efficiency

Sheng¹,

Zhou²,

Yang³

et al. 2020

Preprint

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Aim. The multi-part Unified Parkinson's Disease Rating Scale is the standard instrument in clinical trials. A sum of scores for all items in one or more parts of the instrument is usually analyzed. Without accounting for relative importance of individual items, this sum of scores conceivably does not optimize the power of the instrument. The aim was to compare the ability to detect drug effect in slowing down motor function deterioration, as measured by Part III of the Scale-motor examinationsbetween the item scores and the sum of scores. Methods. We used data from 423 patients in a Parkinson's disease progression trial to estimate the symptom severity by item response modelling; modelled symptom progression using the severity and the sum of scores; and conducted simulations to compare the sensitivity of detecting a broad range of hypothetical drug effects on progression using the severity and the sum of scores. Results. The severity endpoint was far more sensitive than the sum of scores for detecting treatment effects, e.g., requiring 280 versus 570 patients per arm to achieve 60% Probability of Success for detecting a range of potential effects in a 2-year trial. Items related to the left side of the body were most informative; and the domain relevance of tremor items was questionable. Conclusion. This analysis generated clear evidence that longitudinal modelling of item scores can enhance trial efficiency and success. It also prompted the needs for a consensus on the placement of the tremor items in the instrument.

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“…A key assumption for this approach is that all items have equal importance in reflecting the overall disability. 6 In contrast, item response theory (IRT) offers an alternative. It estimates a patient's disease severity (or disability level) as a latent variable directly from the item level scores and with the acknowledgment that each item bears varying importance for informing the disease severity.…”

Section: Introductionmentioning

confidence: 99%

Detecting placebo and drug effects on Parkinson's disease symptoms by longitudinal item‐score models

Chen

Jönsson

Yang

et al. 2021

CPT Pharmacom & Syst Pharma

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This study tested the hypothesis that analyzing longitudinal item scores of the Unified Parkinson's Disease Rating Scale could allow a smaller trial size and describe a drug's effect on symptom progression. Two historical studies of the dopaminergic drug ropinirole were analyzed: a cross‐over formulation comparison trial in 161 patients with early‐stage Parkinson's disease, and a 24‐week, parallel‐group, placebo‐controlled efficacy trial in 393 patients with advanced‐stage Parkinson's disease. We applied item response theory to estimate the patients’ symptom severity and developed a longitudinal model using the symptom severity to describe the time course of the placebo response and the drug effect on the time course. Similarly, we developed a longitudinal model using the total score. We then compared sample size needs for drug effect detection using these two different models. Total score modeling estimated median changes from baseline at 24 weeks (90% confidence interval) of −3.7 (−5.4 to −2.0) and −9.3 (−11 to −7.3) points by placebo and ropinirole. Comparable changes were estimated (with slightly higher precision) by item‐score modeling as −2.0 (−4.0 to −1.0) and −9.0 (−11 to −8.0) points. The treatment duration was insufficient to estimate the symptom progression rate; hence the drug effect on the progression could not be assessed. The trial sizes to detect a drug effect with 80% power on total score and on symptom severity were estimated (at the type I error level of 0.05) as 88 and 58, respectively. Longitudinal item response analysis could markedly reduce sample size; it also has the potential for assessing drug effects on disease progression in longer trials.

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Item Response Model Adaptation for Analyzing Data from Different Versions of Parkinson’s Disease Rating Scales

Cited by 15 publications

References 23 publications

Item Response Theory Analysis of the MDS‐UPDRS Motor Examination: Tremor vs. Nontremor Items

Item Response Theory Analysis of the MDS‐UPDRS Motor Examination: Tremor vs. Nontremor Items

Modelling item scores of Unified Parkinson's Disease Rating Scale for greater trial efficiency

Detecting placebo and drug effects on Parkinson's disease symptoms by longitudinal item‐score models

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