Purpose
To develop a set of consensus and empirically based reporting recommendations for primary studies of the measurement properties of patient-reported outcome measures (PROMs).
Methods
This ...study included four phases: 1. Conducting an extensive literature review of recommendations for reporting of studies testing measurement properties of PROMs; 2. Preparing for the Delphi study by identifying experts; 3. Conducting three Delphi rounds aiming for consensus on the item list of recommendations found in phase 1; 4. Developing the COSMIN reporting guideline and user manual.
Results
The literature review resulted in 93 reporting items, included in the first Delphi round. A total of 84 individuals (from 12 countries) agreed to participate in the Delphi study, with 47, 30 and 25 responding in rounds one, two and three, respectively. After three rounds, we achieved consensus on a set of 71 items separated into a set of 35 "common" items (relevant to all studies on measurement properties) and 41 "specific" items (exclusively relevant to one of the nine measurement properties).
Conclusion
Consensus was achieved on a set of 71 items for inclusion in a reporting guideline for studies on measurement properties of PROMs. These items will guide researchers on the necessary information to include in their reports of investigations of measurement properties of PROMs. This guideline will likely improve the completeness of reporting of these important studies.
Abstract Objectives Patients have their individual minimal important changes (iMICs) as their personal benchmarks to determine whether a perceived health-related quality of life (HRQOL) change ...constitutes a (minimally) important change for them. We denote the mean iMIC in a group of patients as the “genuine MIC” (gMIC). The aims of this paper are (1) to examine the relationship between the gMIC and the anchor-based minimal important change (MIC), determined by receiver operating characteristic analysis or by predictive modeling; (2) to examine the impact of the proportion of improved patients on these MICs; and (3) to explore the possibility to adjust the MIC for the influence of the proportion of improved patients. Study Design and Setting Multiple simulations of patient samples involved in anchor-based MIC studies with different characteristics of HRQOL (change) scores and distributions of iMICs. In addition, a real data set is analyzed for illustration. Results The receiver operating characteristic–based and predictive modeling MICs equal the gMIC when the proportion of improved patients equals 0.5. The MIC is estimated higher than the gMIC when the proportion improved is greater than 0.5, and the MIC is estimated lower than the gMIC when the proportion improved is less than 0.5. Using an equation including the predictive modeling MIC, the log-odds of improvement, the standard deviation of the HRQOL change score, and the correlation between the HRQOL change score and the anchor results in an adjusted MIC reflecting the gMIC irrespective of the proportion of improved patients. Conclusion Adjusting the predictive modeling MIC for the proportion of improved patients assures that the adjusted MIC reflects the gMIC. Limitations: We assumed normal distributions and global perceived change scores that were independent on the follow-up score. Additionally, floor and ceiling effects were not taken into account.
The COSMIN checklist (COnsensus-based Standards for the selection of health status Measurement INstruments) was developed in an international Delphi study to evaluate the methodological quality of ...studies on measurement properties of health-related patient reported outcomes (HR-PROs). In this paper, we explain our choices for the design requirements and preferred statistical methods for which no evidence is available in the literature or on which the Delphi panel members had substantial discussion.
The issues described in this paper are a reflection of the Delphi process in which 43 panel members participated.
The topics discussed are internal consistency (relevance for reflective and formative models, and distinction with unidimensionality), content validity (judging relevance and comprehensiveness), hypotheses testing as an aspect of construct validity (specificity of hypotheses), criterion validity (relevance for PROs), and responsiveness (concept and relation to validity, and (in) appropriate measures).
We expect that this paper will contribute to a better understanding of the rationale behind the items, thereby enhancing the acceptance and use of the COSMIN checklist.
Background The COSMIN checklist is a standardized tool for assessing the methodological quality of studies on measurement properties. It contains 9 boxes, each dealing with one measurement property, ...with 5-18 items per box about design aspects and statistical methods. Our aim was to develop a scoring system for the COSMIN checklist to calculate quality scores per measurement property when using the checklist in systematic reviews of measurement properties. Methods The scoring system was developed based on discussions among experts and testing of the scoring system on 46 articles from a systematic review. Four response options were defined for each COSMIN item (excellent, good, fair, and poor). A quality score per measurement property is obtained by taking the lowest rating of any item in a box ("worst score counts"). Results Specific criteria for excellent, good, fair, and poor quality for each COSMIN item are described. In defining the criteria, the "worst score counts" algorithm was taken into consideration. This means that only fatal flaws were defined as poor quality. The scores of the 46 articles show how the scoring system can be used to provide an overview of the methodological quality of studies included in a systematic review of measurement properties. Conclusions Based on experience in testing this scoring system on 46 articles, the COSMIN checklist with the proposed scoring system seems to be a useful tool for assessing the methodological quality of studies included in systematic reviews of measurement properties.
The anchor-based minimal important change (MIC), based on the receiver operating characteristic (ROC) analysis or predictive modeling, is biased by the proportion of improved patients. The adjusted ...MIC, published in 2017, adjusts the predictive MIC for this bias but does not take the reliability of the transition ratings (i.e., the anchor) into account. The aim of this study was to examine whether the transition ratings reliability affects the accuracy of the adjusted MIC and, if so, whether the adjustment can be improved.
Multiple simulations of patient samples involved in anchor-based MIC studies with different characteristics of patient-reported outcome scores were used to determine the impact of reliability of the transition ratings on the MIC estimate. An improved adjustment formula was derived in an exploration set of simulated samples (number of samples = 19,440) and validated in a different set of simulated samples (number of samples = 12,960). The effect of sample size (100–1,000) was also evaluated in simulated datasets.
Reliability of the transition ratings biased the MIC estimate if the proportion improved was different from 0.5. The improved adjustment formula performed well, especially if the proportion improved was between 0.3 and 0.7. Smaller sample sizes were at the expense of the precision of the MIC estimates.
We provide an improved formula for calculating the adjusted MIC, taking into account the proportion of improved patients and the reliability of the transition ratings.
In cooperation with the Core Outcome Measures in Effectiveness Trials (COMET) initiative, the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) initiative aimed ...to develop a guideline on how to select outcome measurement instruments for outcomes (i.e., constructs or domains) included in a "Core Outcome Set" (COS). A COS is an agreed minimum set of outcomes that should be measured and reported in all clinical trials of a specific disease or trial population.
Informed by a literature review to identify potentially relevant tasks on outcome measurement instrument selection, a Delphi study was performed among a panel of international experts, representing diverse stakeholders. In three consecutive rounds, panelists were asked to rate the importance of different tasks in the selection of outcome measurement instruments, to justify their choices, and to add other relevant tasks. Consensus was defined as being achieved when 70 % or more of the panelists agreed and when fewer than 15 % of the panelists disagreed.
Of the 481 invited experts, 120 agreed to participate of whom 95 (79 %) completed the first Delphi questionnaire. We reached consensus on four main steps in the selection of outcome measurement instruments for COS: Step 1, conceptual considerations; Step 2, finding existing outcome measurement instruments, by means of a systematic review and/or a literature search; Step 3, quality assessment of outcome measurement instruments, by means of the evaluation of the measurement properties and feasibility aspects of outcome measurement instruments; and Step 4, generic recommendations on the selection of outcome measurement instruments for outcomes included in a COS (consensus ranged from 70 to 99 %).
This study resulted in a consensus-based guideline on the methods for selecting outcome measurement instruments for outcomes included in a COS. This guideline can be used by COS developers in defining how to measure core outcomes.
The COMET Handbook: version 1.0 Williamson, Paula R; Altman, Douglas G; Bagley, Heather ...
Trials,
06/2017, Letnik:
18, Številka:
Suppl 3
Journal Article
Recenzirano
Odprti dostop
The selection of appropriate outcomes is crucial when designing clinical trials in order to compare the effects of different interventions directly. For the findings to influence policy and practice, ...the outcomes need to be relevant and important to key stakeholders including patients and the public, health care professionals and others making decisions about health care. It is now widely acknowledged that insufficient attention has been paid to the choice of outcomes measured in clinical trials. Researchers are increasingly addressing this issue through the development and use of a core outcome set, an agreed standardised collection of outcomes which should be measured and reported, as a minimum, in all trials for a specific clinical area.Accumulating work in this area has identified the need for guidance on the development, implementation, evaluation and updating of core outcome sets. This Handbook, developed by the COMET Initiative, brings together current thinking and methodological research regarding those issues. We recommend a four-step process to develop a core outcome set. The aim is to update the contents of the Handbook as further research is identified.
Patient-Reported Outcomes Measurement Information System (PROMIS
®
) is commonly used across medical conditions. To facilitate interpretation of scores across countries, we calculated Dutch reference ...values for PROMIS Physical Function (PROMIS-PF), Pain Interference (PROMIS-PI), Pain Behavior (PROMIS-PB), Ability to Participate in Social Roles and Activities (PROMIS-APSRA), and Satisfaction with Social Roles and Activities (PROMIS-SSRA), as compared to US reference values.
A panel completed full PROMIS-PF (n=1310), PROMIS-PI and PROMIS-PB (n=1052), and PROMIS-APSRA and PROMIS-SSRA (n=1002) item banks and reported their level of health per domain (no, mild, moderate, severe limitations). T-scores were calculated by sample and subgroups (age, gender, self-reported level of domain). Distribution-based and anchor-based thresholds for mild, moderate, and severe scores were determined.
Mean T-scores were close to the US mean of 50 for PROMIS-PF (49.8) and PROMIS-APSRA (50.6), lower for PROMIS-SSRA (47.5) and higher for PROMIS-PI (54.9) and PROMIS-PB (52.0). Distribution-based thresholds for mild, moderate, and severe scores were comparable to US recommended cut-off values (except for PROMIS-PI) but participants reported limitations 'earlier' than suggested thresholds.
Dutch reference values were close to US reference values for some PROMIS domains but not all. We recommend country-specific reference values to facilitate worldwide PROMIS use.
KEY MESSAGES
PROMIS offers universally applicable IRT-based efficient and patient-friendly measures to assess commonly relevant patient-reported outcomes across medical conditions.
To support the use of PROMIS in daily clinical practice and research across the world, country-specific general population reference values should be obtained.
More research is necessary to obtain reliable and valid cut-off values for what constitutes mild, moderate and severe scores from the patients' perspective.
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