SUMMARY Objective Total joint replacement has been proposed as an endpoint in disease modifying osteoarthritis drug (DMOAD) randomized clinical trials (RCTs); however, disparities have generated concerns regarding this outcome. A combined Osteoarthritis Research Society International (OARSI)/Outcome Measures in Rheumatology (OMERACT) initiative was launched in 2004 to develop a composite index [‘virtual total joint replacement’ (VJR)] as a surrogate outcome for osteoarthritis (OA) progression in DMOAD RCTs. Our objective was to evaluate the prevalence of patients fulfilling different thresholds of sustained pain, reduced function, and X-ray change in existing DMOAD RCTs. Design Post hoc analysis of summary data from the placebo arm of eight DMOAD RCTs. Results Eight OA RCTs representing 1379 patients were included. Pain was assessed by WOMAC and/or VAS and function by WOMAC and/or Lequesne. Among six knee and two hip studies, 248 (22%) and 132 (51%) patients respectively had X-ray progression [decrease joint space width (JSW) ≥0.5 mm]. The prevalence of patients fulfilling clinical and radiographic criteria was highest (n = 163, 12%) in the least stringent scenario (pain + function ≥80 at ≥2 visits); with few patients (n = 129, 2%) in the most stringent scenario (pain + function ≥80 at ≥4 visits). Using these prevalence data, a sample size of 352–2144 per group would be needed to demonstrate a 50% difference between groups. Conclusions The prevalence of patients with sustained symptomatic OA of at least a moderate degree with X-ray progression is low. Even using lenient criteria to define VJR, large patient numbers would be required to detect differences between groups in DMOAD RCTs. Investigation of the optimal cutoff threshold and combination of symptoms and radiographic change should be pursued.
Estimation and inference are two key components toward the solution of any statistical problem; however, the inferential issues of statistical assessment of agreement among two or more raters have not been well developed as compared to the development of estimation procedures in this area. The fundamental reason for this gap is the complex expression of the concordance correlation coefficient (CCC) that is frequently used in assessing agreement among raters.Large sample-based statistical tests for CCC often fail to produce desired results for small samples. Hence, inferential procedures for small samples are urgently needed to evaluate agreement between raters. We argue that hypothesis testing of CCC has little value in practice due to the absence of a gold standard of agreement. In this article, we construct the generalized confidence interval (GCI) for CCC utilizing a bivariate normal distribution of measurements, and also develop a large sample-based confidence interval (LSCI). We establish satisfactory performance of GCI by providing the desired coverage probability (CP) via simulation. Results of GCI and LSCI are illustrated and compared with a data set of a recent study performed at U.S. Department of Veterans Affairs, Hines.
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