Dataset Information

Approaches to optimize analyses of multidimensional ordinal MRI data in osteoarthritis research: A perspective.

ABSTRACT:

Objective

Knee osteoarthritis (OA) is a disease of the whole joint involving multiple tissue types. MRI-based semi-quantitative (SQ) scoring of knee OA is a method to perform multi-tissue joint assessment and has been shown to be a valid and reliable way to measure structural multi-tissue involvement and progression of the disease. While recent work has described how SQ scoring may be used for clinical trial enrichment and disease phenotyping in OA, less guidance is available for how these parameters may be used to assess study outcomes.

Design

Here we present recommendations for summarizing disease progression within specific tissue types. We illustrate how various methods may be used to quantify longitudinal change using SQ scoring and review examples from the literature.

Results

Approaches to quantify longitudinal change across subregions include the count of number of subregions, delta-subregion, delta-sum, and maximum grade changes. Careful attention should be paid to features that may fluctuate, such as bone marrow lesions, or with certain interventions, for example pharmacologic interventions with anticipated cartilage anabolic effects. The statistical approach must align with the nature of the outcome.

Conclusions

SQ scoring presents a way to understand disease progression across the whole joint. As OA is increasingly recognized as a heterogeneous disease with different phenotypes a better understanding of longitudinal progression across tissue types may present an opportunity to match study outcome to patient phenotype or to treatment mechanism of action.

SUBMITTER: Collins JE

PROVIDER: S-EPMC11004399 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature

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Publications

Approaches to optimize analyses of multidimensional ordinal MRI data in osteoarthritis research: A perspective.

Collins Jamie E JE Roemer Frank W FW Guermazi Ali A

Osteoarthritis and cartilage open 20240327 2

<h4>Objective</h4>Knee osteoarthritis (OA) is a disease of the whole joint involving multiple tissue types. MRI-based semi-quantitative (SQ) scoring of knee OA is a method to perform multi-tissue joint assessment and has been shown to be a valid and reliable way to measure structural multi-tissue involvement and progression of the disease. While recent work has described how SQ scoring may be used for clinical trial enrichment and disease phenotyping in OA, less guidance is available for how the ...[more]

PMID: 38601258

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Project description:BackgroundMeasures in nursing research frequently use Likert scales that yield ordinal data. Confirmatory factor analysis using Pearson correlations commonly applies to such data, although this violates ordinal scale assumptions.ObjectivesThe aim of this study was to illustrate the application of polychoric correlations and polychoric confirmatory factor analysis as a valid alternative statistical approach using data on family members' perceived support from nurses as an exemplar.MethodsA primary analysis of cross-sectional data from a sample of 800 participants using data collected with the Iceland-Family Perceived Support Questionnaire was conducted using polychoric versus Pearson correlations, analysis of variance, and confirmatory factor analysis.ResultsA two-factor measurement model was compatible with data from family members in the Ugandan care settings. Two contextual factors (cognitive and emotional support) constituted the family support measurement model. A factor correlation indicated that the two factors reflected distinct but closely related aspects of family support. Polychoric correlation revealed 13.8% (range: 5.5%-25.2%) higher correlations compared to Pearson correlations. Moreover, the polychoric agreed with the data, whereas the Pearson confirmatory factor analysis did not fit based on multiple statistical criteria. Analyses indicated a difference in emotional and cognitive support perception across two family characteristics: education and relationship to the patient.DiscussionA polychoric correlation suggests stronger associations, and consequently, the approach can be more credible with an ordinal Likert scale than Pearson correlations. Hence, polychoric confirmatory factor analysis can address a larger proportion of variance. In nursing research, polychoric confirmatory factor analysis can confidently be utilized when conducting confirmatory factor analysis of ordinal variables in Likert scales. Furthermore, when a Pearson confirmatory factor analysis is used for ordinal Likert scales, the researcher should carefully evaluate the difference between the two approaches and justify their methodological choice. Even though we do not suggest dispensing with Pearson correlations entirely, we recommend using polychoric correlation for ordinal Likert scales.

Project description:ObjectivesMethods for pharmacoepidemiologic studies of large-scale data repositories are established. Although clinical cohorts of older adults often contain critical information to advance our understanding of medication risk and benefit, the methods best suited to manage medication data in these samples are sometimes unclear and their degree of validation unknown. We sought to provide researchers, in the context of a clinical cohort study of delirium in older adults, with guidance on the methodological tools to use data from clinical cohorts to better understand medication risk factors and outcomes.DesignProspective cohort study.SettingThe Successful Aging After Elective Surgery (SAGES) prospective cohort.ParticipantsA total of 560 older adults (aged ≥70 years) without dementia undergoing elective major surgery.MeasurementsUsing the SAGES clinical cohort, methods used to characterize medications were identified, reviewed, analyzed, and distinguished by appropriateness and degree of validation for characterizing pharmacoepidemiologic data in smaller clinical data sets.ResultsMedication coding is essential; the American Hospital Formulary System, most often used in the United States, is not preferred over others. Use of equivalent dosing scales (e.g., morphine equivalents) for a single medication class (e.g., opioids) is preferred over multiclass analgesic equivalency scales. Medication aggregation from the same class (e.g., benzodiazepines) is well established; the optimal prevalence breakout for aggregation remains unclear. Validated scale(s) to combine structurally dissimilar medications (e.g., anticholinergics) should be used with caution; a lack of consensus exists regarding the optimal scale. Directed acyclic graph(s) are an accepted method to conceptualize causative frameworks when identifying potential confounders. Modeling-based strategies should be used with evidence-based, a priori variable-selection strategies.ConclusionAs highlighted in the SAGES cohort, the methods used to classify and analyze medication data in clinically rich cohort studies vary in the rigor by which they have been developed and validated.

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Dataset Information

Approaches to optimize analyses of multidimensional ordinal MRI data in osteoarthritis research: A perspective.

Objective

Design

Results

Conclusions

Publications

Approaches to optimize analyses of multidimensional ordinal MRI data in osteoarthritis research: A perspective.

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