Common measures or common metrics? A plea to harmonize measurement results

Abstract

Objective: There is a great variety of measurement instruments to assess similar constructs in clinical research and practice. This complicates the interpretation of test results and hampers the implementation of measurement-based care.Method: For reporting and discussing test results with patients, we suggest converting test results into universally applicable common metrics. Two well-established metrics are reviewed: T scores and percentile ranks. Their calculation is explained, their merits and drawbacks are discussed, and recommendations for the most convenient reference group are provided. Results:We propose to express test results as T scores with the general population as reference group. To elucidate test results to patients, T scores may be supplemented with percentile ranks, based on data from a clinical sample. The practical benefits are demonstrated using the published data of four frequently used instruments for measuring depression: the CES-D, PHQ-9, BDI-II and the PROMIS depression measure.Discussion: Recent initiatives have proposed to mandate a limited set of outcome measures to harmonize clinical measurement. However, the selected instruments are not without flaws and, potentially, this directive may hamper future instrument development. We recommend using common metrics as an alternative approach to harmonize test results in clinical practice, as this will facilitate the integration of measures in day-to-day practice.

Evaluating a web‐based personalized decision report for total knee or hip replacement: Lessons learned from patients

“…This is an example of the challenge of complex feedback (i.e., presenting scores from multiple PROs) 36 and supports a call for common metrics (e.g., presenting all data as T-scores) to simplify communication to end users. 37 Additionally, research on when misinterpretations disrupt clinical benefits of patient-facing PRO feedback is warranted to understand how precise patient interpretation needs to be in specific decision-making contexts. This understanding can optimize matching of data presentation and any corresponding explanations to the necessary degree of precision.…”

Norms and T-scores for screeners of alcohol use, depression and anxiety in the population of Suriname

“…We established norm tables in order to give meaning to scale scores. Finally, we established for all scales cross-walk tables to convert raw scores to a common metric: T-scores ( 25 ). These T-scores were based on theta’s from IRT models.…”

The Saudi-Arabic adaptation of the Body Shape Questionnaire (BSQ34): Psychometrics and norms of the full version and the short version (BSQ8C)

“…Internal consistency of the BSQs was measured by Cronbach’s α (α ≥ 0.70 was considered good and α ≥ 0.90 excellent; Cronbach, 1951 ; Gliem and Gliem, 2003 ) as well as McDonald’s ω ( MacDonald, 1999 ). In addition, an IRT-based transformation of scores was performed, as described elsewhere ( de Beurs et al, 2022a , b ). First, an IRT model was fitted to the data, and factor scores (theta’s) with M = 0 and SD = 1 were calculated.…”

“…In addition, a valid Saudi- Arabic BSQ could be used to measure reduction of eating disorder symptoms after eating disorder treatment. Furthermore, based on an Item Response Theory (IRT) analysis, factor scores can be used to obtain normalized standard scores ( T -scores) and to establish percentile scores, both will offer a conversion of raw scores into these common metrics, which will ease interpretation and increase applicability of the measure ( de Beurs et al, 2022a ).…”