Objective: Radiographic joint space width (JSW) has been a standard for measuring knee osteoarthritis (OA) structural change. Limitations in the responsiveness of this approach might be overcome by instead measuring 3D JSW on weight-bearing CT (WBCT). This study compared the responsiveness of 3D JSW measurements using WBCT with the responsiveness of radiographic 2D JSW.Design: Standing, fixed -flexion knee radiographs (XR) and WBCT were acquired ancillary to the 144-and 168-month Multicenter Osteoarthritis Study visits. Tibiofemoral JSW was measured on both XR and WBCT. Responsiveness to change was defined by the standardized response mean (SRM) for change in JSW (1) at predetermined mediolateral locations (JSWx) on both modalities and (2) in the following subregions measured on WBCT images: central medial and lateral femur (CMF/CLF) and tibia (CMT/CLT), and anterior and posterior tibia (AMT/ALT, PMT/MLT).Results: Baseline and 24-month follow-up JSWx measurements were completed for 265 participants (58.1% women). Responsiveness of 3D JSWx for medial tibiofemoral compartment on coronal WBCT (SRM range:-0.18,-0.24) exceeded that for 2D JSWx (-0.10,-0.16). Responsiveness of 3D JSW sub -regional mean (-0.06,-0.36) and maximal (-1.14,-1.75) CMF and CMT and maximal CLF/CLT 3D JSW changes were statistically significantly greater in comparison with respective medial and lateral 2D JSWx (P < 0.002).Conclusions: Subregional 3D JSW on WBCT is substantially more responsive to 24-month changes in tibiofemoral joint structure compared to radiographic measurements. Use of subregional 3D JSW on WBCT could enable improved detection of OA structural progression over a 24-month duration in comparison with measurements made on XR.(c) 2022 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

For sample size calculation in clinical trials with survival endpoints, the logrank test, which is the optimal method under the proportional hazard (PH) assumption, is predominantly used. In reality, the PH assumption may not hold. For example, in immuno-oncology trials, delayed treatment effects are often expected. The sample size without considering the potential violation of the PH assumption may lead to an underpowered study. In recent years, combination tests such as the maximum weighted logrank test have received great attention because of their robust performance in various hazards scenarios. In this paper, we propose a flexible simulation-free procedure to calculate the sample size using combination tests. The procedure extends the Lakatos' Markov model and allows for complex situations encountered in a clinical trial, like staggered entry, dropouts, etc. We evaluate the procedure using two maximum weighted logrank tests, one projection-type test, and three other commonly used tests under various hazards scenarios. The simulation studies show that the proposed method can achieve the target power for all compared tests in most scenarios. The combination tests exhibit robust performance under correct specification and misspecification scenarios and are highly recommended when the hazard-changing patterns are unknown beforehand. Finally, we demonstrate our method using two clinical trial examples and provide suggestions about the sample size calculations under nonproportional hazards.

Fisher's exact test and Pearson's chi-squared test are frequently used for testing associations of two binary variables in 2-by-2 contingency tables. In the single test setting, many studies have shown that the asymptotic Pearson's chi-squared test cannot preserve the test size for small samples and Fisher Exact test tends to be overly conservative. Multiple unconditional exact tests were proposed for small samples as they perform better than the commonly used chi-square and Fisher's exact test. No comparison of these approaches have been done in the multiple testing setting.This study examines the performances of two unconditional tests (Boschloo and Z-pooled test statistics are used) with Fisher's exact test as well as asymptotic Pearson's chi-squared test in a small sample multiple testing scenario via a simulation study. When testing simultaneously many null hypotheses, Benjamini-Hochberg (BH) procedure is typically applied to control the false discovery rate (FDR). The results show that in terms of sensitivity rate, the performances of Z-pooled and Boschloo Statistic are close to each other; Asymptotic chi-squared test is slightly better than the unconditional exact tests; Fisher's exact test is the least powerful in all different settings. Boschloo's test is more computation intensive. Z-pooled test is preferred if running time is a concern.

Radiography is the current standard for assessing osseous structural knee OA, but it is a 2D image of a 3D structure and superimposition of bone impairs ability to sensitively detect changes. Weight-bearing CT (WBCT) allows improved visualization of OA features, shows greater correlation with central medial cartilage damage on MRI and has demonstrated excellent test-retest reliability, suggesting that WBCT may address shortcomings of radiographs. To determine the concurrent validity of change in 3D JSW on WBCT vs. change in 2D JSW on radiographs with radiographic progression defined by semiquantitative grading using of cartilage damage worsening on MRI (MOAKS) over 24 months. Radiographs, WBCT and MRI were obtained from 259 participants from the Multicenter Osteoarthritis Study (MOST) at baseline and 24-month follow-up (including one knee per participant). Radiographs were evaluated for change in 2D JSW at predetermined locations in the medial and lateral compartments. Change in 3D JSW on WBCT assessed JSW in each subregion (Figure). MRI scans were assessed using MRI Osteoarthritis of the Knee Score for cartilage morphology (MOAKS-CM). Concurrent validity was assessed by associations of JSW change on radiographs and WBCT with structural worsening of MOAKS-CM using logistic regression. Area under the receiver operating characteristic (AUROC) curve was calculated to compare the concurrent validity of 2D JSW vs. 3D JSW by compartment and subregion. A "no exposure" model was reported using only demographic variables. Exposure variables in each model contained either 2D JSWx or 3D JSW. Two separate parameter types were utilized for 3D JSW exposure, maximum change values and change in mean values. In addition to the main exposure JSW parameter, each model adjusted for demographic covariates (age, sex and BMI). JSWx=.225 was used when comparing to medial 3D JSW subregional change and JSWx=.750 was used when comparing to lateral 3D JSW subregional change. Mean age was 63.2 ± 9.0 years, BMI was 28.2±4.9 kg/m2, 57% were women and knees had KL Grade 2-3 at baseline for 60/259. For change in medial compartment MOAKS-CM, 3D Mean (AUROC=0.681) and Maximum (AUROC=0.682) were not superior to 2D JSW (AUROC=0.713). For change in lateral compartment MOAKS-CM, 3D Mean (AUROC=0.757) and Maximum (AUROC=0.730) showed no statistical difference with 2D JSW (AUROC=0.695). In subregional analyses, there was no significant difference between the association of change in MOAKS-CM and change in either 3D JSW (Mean, Maximum) or 2D JSW. In this study of the association of knee OA structural progression assessed by cartilage morphology on MRI, change in 3D JSW parameters measured on WBCT and 2D JSW on radiographs, WBCT did not demonstrate superior concurrent validity with worsening MOAKS-CM compared to change in 2D JSW assessed on knee radiographs. Thus, diagnostic value of this implementation of 3D JSW remained similar to that for 2D JSW. National Institutes of Health, University of Kansas (R01AR071648), University of Iowa (U01AG18832) and University of California-San Francisco (U01AG19069). AG and FWR are shareholders of BICL, LLC. AG is consultant to Pfizer, MerckSerono, TissueGene, Novartis, Regeneron and AstraZeneca. FWR is consultant to Grünenthal. NS is a consultant for Integra BioLife, Trice Medical and Pacira Biosciences. Others authors have no conflicts of interest to disclose. The authors would like to thank participants and staff of the MOST study. CORRESPONDENCE ADDRESS: [email protected]

Social determinants of health are conditions that influence an individual's health. Investigators explored associations between social needs and type 2 diabetes (T2DM) diagnoses through retrospective chart review (October 2017-Septemeber 2018) and statistical analyses of an 11-domain social needs questionnaire routinely administered in a large health system in Kansas City, Kansas (n = 26,093, temporal relationship between diagnoses and screening undetermined). Except for childcare needs, all social needs were more commonly reported in patients with a T2DM diagnosis. Domains with the strongest associations were prescription cost, transportation, and health literacy. These findings may inform health system and social service provider partnerships to offer assistance in specific domains.

In practice, the logrank test is the most widely used method for testing the equality of survival distributions. It is the optimal method under the proportional hazard assumption. However, since non-proportional hazards are often encountered in oncology trials, alternative tests have been proposed. The maximum weighted logrank test was shown to be robust in general situations. In this manuscript, we propose a new maximum test that incorporates the weight for detecting crossing hazards. The new weight is a function of the crossing time-point. Extensive simulation studies are conducted to compare our methods with other methods proposed in the literature under scenarios with various hazard ratio patterns, sample sizes, censoring rates, and censoring patterns. For crossing hazards, the proposed test is shown to be the most powerful one with a known crossing time-point. It has a similar performance as the Maxcombo test in the misspecified crossing time-point scenario. Under other alternative situations, the new test remains comparatively powerful as the Maxcombo test. Finally, we illustrate the test in a real data example and discuss the procedures to extend the test to detect crossing hazards specifically.