Pain intensities of patients are repeatedly measured by Visual Analog Scale (VAS) and Pain Vision (PV) in a clinical research. Two measurements by VAS and PV are performed at the same time. In order to evaluate within patient consistency, intra-individual coefficient of variations (CVs) are compared between measures assuming that the pain status of each patient is stable during the research period. The correlated samples and different inter-individual variation due to different scales of the measures should be taken into account in statistical analysis. The adjustment of covariates will improve the estimation of population mean values of the measures. In this paper, statistical approach to compare the intra-individual CVs is proposed. The approach consists of two steps: (1) estimating population mean values and intra-individual variances of the pain intensities by measure in a mixed effect model framework, (2) computing intra-individual CVs and comparing them between measures. The mixed effect model includes measure and some variables as fixed effects and subject by measure as a random effect. The different inter-individual variations between measures and their covariance reflect the paired sampling in the variance component. The confidence interval of the difference of intra-individual CVs is constructed using the asymptotic normality and the delta method. Bootstrap method is available if sample size is small. The proposed approach is illustrated using pain research data. Measure (VAS and PV), age and sex are included in the model as fixed effects. The confidence intervals of the difference of intra-individual CVs between measures are estimated by the asymptotic theory and by bootstrap using a subgroup resampling, respectively. Both confidence intervals are similar. The proposed approach is useful to compare two intra-individual CVs taking it into account to reflect the paired sampling, different inter-individual variations between measures and some covariates. Although the inclusion of covariates did not improve the goodness-of-fit in the illustration, the proposed model with covariates will improve the accuracy and/or precision if covariates truly influence response variable. This approach can be applicable with small modification to various situations.

The monosubstituted ferrocene derivatives, ω-[4-(4-methoxyphenoxycarbonyl) phenoxy]alkoxycarbonyl ferrocene, were synthesized in order to compare the liquid crystallinity to that of disubstituted ferrocene derivatives, 1,1′-bis[ω-[4-(4-methoxy-phenoxycarbonyl)phenoxy]alkoxycarbonyl]ferrocene. Some of the monosubstituted ferrocene derivatives containing relatively longer flexible spacer showed the liquid crystalline phase, nematic and/or smectic phases. The crystal structure of 2-[4-(4-methoxyphenoxycarbonyl)phenoxy]ethoxycarbonyl ferrocene was determined by X-ray diffraction method using a single crystal. The molecular structure revealed a bent structure that is almost a half of “Z” shaped molecular structure of 1,1′-bis[2-[4-(4-methoxyphenoxycarbonyl)phenoxy]ethoxycarbonyl]ferrocene.

We developed a computer-aided detection (CADe) system to detect and localize colorectal lesions by modifying You-Only-Look-Once version 3 (YOLO v3) and evaluated its performance in two different settings. The test dataset was obtained from 20 randomly selected patients who underwent endoscopic resection for 69 colorectal lesions at the Jikei University Hospital between June 2017 and February 2018. First, we evaluated the diagnostic performances using still images randomly and automatically extracted from video recordings of the entire endoscopic procedure at intervals of 5 s, without eliminating poor quality images. Second, the latency of lesion detection by the CADe system from the initial appearance of lesions was investigated by reviewing the videos. A total of 6531 images, including 662 images with a lesion, were studied in the image-based analysis. The AUC, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.983, 94.6%, 95.2%, 68.8%, 99.4%, and 95.1%, respectively. The median time for detecting colorectal lesions measured in the lesion-based analysis was 0.67 s. In conclusion, we proved that the originally developed CADe system based on YOLO v3 could accurately and instantaneously detect colorectal lesions using the test dataset obtained from videos, mitigating operator selection biases.

When subjects may fail from several distinct causes but the occurrence of failure from one precludes the others, it is referred competing risks. While both event of interest and competing event types are unfavorable in many typical settings, there are some cases in which a favorable event and unfavorable event types are competing. We extend an application of competing risks model to a case in IgA nephropathy, in which two types of events were negatively created but not satisfy the condition of competing risks. Each event type does not necessarily preclude the other type of event and the exact order of time to events was often unclear due to irregular censoring intervals. We discuss interpretation of the result and generalization of our extension.

A variety of statistical methods for interval-censored data have been actively researched and developed in various disease areas since 1970s, when research on the new infectious disease HIV/AIDS attracted attention. In oncology, the data are obtained as partially interval-censored, such as progression free survival time, where the application of the usual survival data analysis method is often performed after imputing right-point of the censoring interval. However, other single point imputation methods such as left-point or midpoint also could be candidates, and a lot of research has been done on the statistical properties of single/multiple imputations, but it was limited to only simulation studies. In our study, we examine the characteristics of deterministic (left-, mid, and right-point) imputations from a theoretical aspect, and show that midpoint imputation is the most preferable if the true distribution is the exponential distribution. In our simulation, we compared the performance of these three deterministic single-point imputation methods and Finkelstein’s proportional hazards model without imputation in the setting of the selection design, so called a pick-the-winner design, in which the most promising treatment is identified based on the point estimate. The correct selection probability and performance measures of a regression coefficient were used for evaluation criteria. Midpoint imputation or left-point imputation was shown to be the best depending on the simulation condition.

The Oxford (split) and Japanese Histological Grade (JHG [lumped]) classifications of immunoglobulin A nephropathy (IgAN) are widely used in Japan. We compared their prognostic ability and explored the benefits of combining them. Data were from a prospective IgAN cohort (2005–2015). The primary endpoint was a ≥ 50% decline in estimated glomerular filtration rate (eGFR) or end-stage renal disease. Cox regression with Uno C assessed prognostic discrimination. The integrated discrimination improvement index evaluated the added value of histologic to clinical variables (eGFR, urinary protein, and mean arterial pressure) at 1, 5, and 10 years. Kaplan–Meier analysis stratified patients into JHG 1 and 2–4 to assess the prognostic value of mesangial hypercellularity (M) and interstitial fibrosis/tubular atrophy (T). Among 938 patients (median follow-up: 66 months), 58 (6.2%) reached the endpoint. M and T (Oxford) and JHG were significantly associated with outcomes. Both systems showed good discrimination (Uno C: 0.83 and 0.86). Adding Oxford to clinical variables improved prediction at 5 and 10 years; adding JHG improved prediction at 1, 5, and 10 years. M and T were predictive in JHG 2–4 but not in JHG 1. Both classifications demonstrated comparable discrimination. Their combined use can help enhance predictive precision.