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Objective: To determine the content, structural, and construct validity of the Dutch version of the Neck Disability Index (NDI). Study Design and Setting: To assess content validity, 11 neck pain experts and 10 patients commented on the construct, comprehensiveness, and relevance of the NDI. Structural validity was assessed by item factor analysis (FA) and item response theory modeling using the generalized partial credit model. Differential item functioning (DIF) analysis for gender was examined. Pearson correlation coefficient with the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire was calculated to assess construct validity. Results: In addition to a suboptimal translation, we found a lack of consensus on the construct the NDI intends to measure. Experts and patients suggested that the NDI measures more than physical functioning. Unidimensionality of the NDI could not be confirmed. DIF analysis for gender showed DIF for the headache item. The goodness-of-fit statistics for FA with one factor were satisfactory when the item ‘‘concentration’’ was omitted. A correlation of 0.75 with the DASH was found supporting construct validity. Conclusion: It is questionable whether in research the NDI should be the instrument of choice for use as a primary outcome measure. Definition of the construct to be measured is a prerequisite for the assessment of validity. 2013 Elsevier Inc. All rights reserved.

Objective: Lack of consensus on taxonomy, terminology, and definitions has led to confusion about which measurement properties are relevant and which concepts they represent. The aim was to clarify and standardize terminology and definitions of measurement properties by reaching consensus among a group of experts and to develop a taxonomy of measurement properties relevant for evaluating health instruments. Study Design and Setting: An international Delphi study with four written rounds was performed. Participating experts had a background in epidemiology, statistics, psychology, and clinical medicine. The panel was asked to rate their (dis)agreement about proposals on a five-point scale. Consensus was considered to be reached when at least 67% of the panel agreed. Results: Of 91 invited experts, 57 agreed to participate and 43 actually participated. Consensus was reached on positions of measurement properties in the taxonomy (68e84%), terminology (74e88%, except for structural validity [56%]), and definitions of measurement properties (68e88%). The panel extensively discussed the positions of internal consistency and responsiveness in the taxonomy, the terms ‘‘reliability’’ and ‘‘structural validity,’’ and the definitions of internal consistency and reliability. Conclusions: Consensus on taxonomy, terminology, and definitions of measurement properties was reached. Hopefully, this will lead to a more uniform use of terms and definitions in the literature on measurement properties. 2010 Elsevier Inc. All rights reserved.

Objective: Determining "minimally important change" (MIC) facilitates the interpretation of change scores on multi-item instruments. This article focuses on how MIC values should be interpreted when applied to individual patients. Study Design and Setting: The MIC value of a hypothetical questionnaire "Q" was determined in a sample of 400 patients who improved and 100 patients who did not improve, using the receiver operating characteristic (ROC) method, and three methods to quantify the uncertainty. Results: The MIC value on questionnaire Q was 10.5. Firstly, the 95% confidence interval (CI) of the MIC value (for questionnaire Q: 5.6-14.2) quantifies the uncertainty of the estimation of the MIC value. Secondly, "how sure we are that this MIC value holds for every patient" is quantified by the values for sensitivity (74%) and specificity (91%). Thirdly, the smallest detectable change (SDC) on questionnaire Q is calculated (16.0) to consider whether the MIC value (10.5) falls outside or within the measurement error. Conclusion: For application in clinical research and practice, MIC values are always considered at the individual level, but determined in groups of patients. The interpretation comes with different forms of uncertainty. To appreciate the uncertainty, knowledge of the underlying distributions of change scores is indispensable. © 2010 Elsevier Inc. All rights reserved.

Objective: To provide a practical example showing how to relate measurement error to minimal important change (MIC) to estimate sample sizes required for detecting important changes in research, and to determine the usefulness of patient-reported outcomes (PROs) in daily clinical practice. Study Design and Setting: The standard error of measurement (SEM) can be calculated as a parameter of measurement error. MIC can be determined by taking the mean change in score on the questionnaire in the subgroup of patients that reported to be ‘‘a little better’’ or ‘‘better’’ on a global rating scale minus the mean change in score in the subgroup of patients that reported to be ‘‘not changed.’’ Based on SEM and MIC, formulas are presented that can be used to estimate sample sizes and to determine whether the questionnaire can be used to monitor changes in individual patients in daily clinical practice. Results: A fully worked-out example is presented, using Western Ontario and McMaster University Osteoarthritis Index data from a cohort of patients undergoing hip or knee replacement. Conclusion: We strongly recommend investigators to present the SEM and MIC of PROs and use these to estimate sample sizes for research and to determine the usefulness of these outcomes in daily clinical practice. 2008 Elsevier Inc. All rights reserved.

Background and purpose When evaluating the outcome after total knee arthroplasty (TKA), increasing emphasis has been put on patient satisfaction and ability to perform activities of daily living. To address this, the forgotten joint score (FJS) for assessment of knee awareness has been developed. We investigated the validity and reliability of the FJS. Patients and methods A Danish version of the FJS questionnaire was created according to internationally accepted standards. 360 participants who underwent primary TKA were invited to participate in the study. Of these, 315 were included in a validity study and 150 in a reliability study. Correlation between the Oxford knee score (OKS) and the FJS was examined and test-retest evaluation was performed. A ceiling effect was defined as participants reaching a score within 15% of the maximum achievable score. Results The validity study revealed a strong correlation between the FJS and the OKS (intraclass correlation coefficient (ICC) = 0.81, 95% CI: 0.77–0.85; p < 0.001). The test-retest evaluation showed almost perfect reliability for the FJS total score (ICC = 0.91, 95% CI: 0.88–0.94) and substantial reliability or better for individual items of the FJS (ICC? 0.79). We found a high level of internal consistency (Cronbach’s? = 0.96). The ceiling effect for the FJS was 16%, as compared to 37% for the OKS. Interpretation The FJS showed good construct validity and test-retest reliability. It had a lower ceiling effect than the OKS. The FJS appears to be a promising tool for evaluation of small differences in knee performance in groups of patients with good clinical results after TKA.

Background: In the setting of multiple remission and relapse periods of a chronic disease, simple endpoint analysis does not fully capture all relevant information, and we need methods to additionally describe both the duration of remission as well as the interruptions in this desired state. Probably the two-state continuous Markov process model comprises the best mathematical approach to data analysis. However, this approach is complex and not intuitive to clinicians. In this paper we propose a simple scoring system and a graph that can enhance the information about the remission experience in a trial or cohort study. Methods: The continuity rewarded ('ConRew') score sums up periods in remission, and rewards extended periods by placing more value on uninterrupted periods than on interrupted periods. The 'patient vector graph' attempts to plot each patient's remission experience over time as a horizontal line (the 'vector') that is visible when the patient is in remission, but interrupted whenever relapse occurs. In this way a pattern is formed that conveys the number of patients experiencing remission, their individual total duration and interruptions, and time when these occur. Results: In a dataset of a randomized trial in early rheumatoid arthritis, the graph clearly showed both early and late benefit of one group over the other. The scoring system demonstrated the main benefit was in the number of remission periods, not in their 'uninterruptedness'. Conclusion: Both approaches proved feasible and added extra information. © 2010 Elsevier Inc. All rights reserved.