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Evaluation of a severity grade (SG) is important to classify patients for efficient use of limited medical resources. This study validates two existing evaluation systems for the prevention of the coronavirus disease 2019 (COVID-19) in Japan: a criterion of SG and a list of 14 specialized underlying diseases (SUDs). A retrospective cohort was created using electronic medical records from 18 research institutes. The cohort includes 6,050 COVID-19 patients with two types of diagnosis information as follows: SG at hospitalization among mild, moderate I, moderate II, and severe and aggravation after hospitalization. A crude mortality rate and an aggravation rate increased by the worsening of SG in the COVID-19 cohort. The transition of the aggravation rate was notable for COVID-19 patients with SUD. A conditional probability of the mortality given the aggravation in the COVID-19 cohort was 87.4% compared to mild or moderate patients (approximately 21%–45%) who have the possibility of the aggravation. An odds ratio of the mortality and aggravation information about the SUD list was higher than other variables. We demonstrated the possibility of improving the criteria of SG by including the SUD list for more effective operation of the criteria of SG. Furthermore, we demonstrated the importance of the prevention of the aggravation based on the conditional probability, and the possibility of predicting the aggravation using the risk factors. [ABSTRACT FROM AUTHOR]

Multiregional clinical trials (MRCTs) have become increasingly common during the development of new drugs to obtain simultaneous drug approvals worldwide. When planning MRCTs, a major statistical challenge is determination of the regional sample size. In general, the regional sample size must be determined as the sample size such that the regional consistency probability, defined as the probability of meeting the regional consistency criterion, is greater than a prespecified value. The Japanese Ministry of Health, Labour and Welfare proposed two criteria for regional consistency. Moreover, many researchers have proposed corresponding closed-form formulas for calculating regional consistency probabilities when the primary outcome is continuous. Although some researchers have argued that those formulas are also applicable to cases with binary outcomes, it remains questionable whether such an argument can be true. Based on simulation results, we demonstrate that the existing formulas are inappropriate for binary cases, even when the regional sample size is sufficiently large. To address this issue, we develop alternative formulas and use simulation to show that they provide accurate regional consistency probabilities. Furthermore, we present an application of our proposed formulas for an MRCT of advanced or metastatic clear-cell renal cell carcinoma., (© 2023 John Wiley & Sons Ltd.)

This paper presents a statistical modeling approach to explore the dependence of extreme values in multi-site tidal water levels (TL) using hourly data from six tidal stations in the Kansai region, Japan. The proposed method utilizes a multi-site conditional extreme value model (CEVM) and a peak over threshold (POT) model with yearly order grouping (YOG) to handle extreme value dependence and marginal modeling of tidal CEVM, respectively. The results demonstrate the effectiveness of the proposed approach in analyzing extreme value dependence among multiple sites, with the YOG method yielding favorable results for extreme value dependence analysis. The extreme dependence effect of TL and the probability of exceedance at multiple sites under extreme conditions are investigated. The paper concludes that the extreme dependence effect should be captured through a multivariate CEVM and provides a more accurate basis for engineering construction and disaster prevention. The novelty of this work lies in its statistical modeling approach that enables accurate multi-site extreme value analysis and provides a more comprehensive understanding of the extreme value dependence of the TLs, which has significant implications for disaster management and prevention. • A statistical modeling is proposed for multi-site extreme values and dependence. • A POT model under the yearly order grouping is established. • CEVM is developed to study the dependence of multi-site extreme TL in Kansai region. • Extreme dependence is not necessarily related to the overall data correlation. • The failure probability under extreme conditions is discussed by CEVM. [ABSTRACT FROM AUTHOR]

Offshore structures are typically subjected to offshore mainshock (MS)–aftershock (AS) sequences. The realistic statistical modeling of intensity measures (IMs) for offshore MS–AS sequences is crucial for the seismic risk assessment and optimal design of offshore structures. This paper develops a framework to forecast the AS IMs given the offshore MS IMs. In particular, using the IMs of offshore MS–AS seismic motions selected from the Japan's K-NET seismograph network, the standard kernel density estimation (KDE) and transformation KDE (TKDE) (i.e., logarithmic TKDE (log-TKDE) and square root TKDE (sqrt-TKDE)) are employed to estimate the marginal cumulative distributions (MCDs) of IMs. Several bivariate copulas are used to model the joint probability distributions (JPDs). Furthermore, the copula-based conditional distribution is used to calculate the conditional probability for the AS IMs under the conditions of the MS IMs. The results demonstrate the ability of the proposed bivariate model to realistically characterize the statistical characteristic and dependent structures for MS–AS IMs. Finally, given the offshore MS IMs, the copula-based conditional probability can be used to accurately estimate the range and probability of occurrence for offshore AS IMs. • A framework constructed for predicting AS IMs given offshore MS IMs. • Offshore MS IMs and AS IMs were correlated. • Log-TKDE and copula used to estimate MCDs and JPDs. • Copula-based conditional distribution used to calculate probability of AS IMs. [ABSTRACT FROM AUTHOR]

In order to estimate the influence of sources on PM2.5 in the industrial area of Japan, we carried out a source analysis using chemical component data of PM2.5. PM2.5 samples were collected intermittently at an industrial area in Japan from July 2010 to November 2012. Water soluble ions (Cl-, NO3-, SO42-, Na+, NH4+, K+, Mg2+, Ca2+), elements (Al, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Cd, Sb, Pb), and carbonaceous species (OC, EC) of the PM2.5 (a total of 198 samples) were analyzed. Positive Matrix Factorization (PMF) model was applied to the data of those chemical components to identify the source of PM2.5. At this observation site, nine factors were extracted. The major contributors of PM2.5 were secondary sulfate 1, in which loading factors of SO42- and NH4+ were large (percentage source contribution: 20.9%), traffic, in which loading factors of OC (organic carbon) and EC (elemental carbon) were large (20.8%), secondary sulfate 2, in which loading factors of K and SO42- were large (8.0%), steel mills (7.8%), secondary chloride and nitrate (7.0%), soil (5.0%), heavy oil combustion (3.8%), sea salt (3.8%), and coal combustion (2.3%). The conditional probability function (CPF) and the potential source contribution function (PSCF) were carried out to examine the influence of a regional source and a broad-based source, respectively. CPF results supported local source influences such as steel mills, sea salt, traffic, coal combustion, and heavy oil combustion. PSCF results suggested that ships in the East China Sea, an industrial area of the east coastal region of China, and an active volcano in the Kyushu region of Japan were potential regional sources of secondary sulfate 1. Secondary sulfate 2 was affected by the burning of biomass fields and by coal combustion in Chinese urban areas such as Beijing, Hebei, and western Inner Mongolia. Source characterization using continuous data from one site showed a potential source representing fossil fuel combustion is affected both by regional and broad-based sources. [ABSTRACT FROM AUTHOR]

Abstract: To elucidate allometric scaling in complex systems, we investigated the underlying scaling relationships between typical three-scale indicators for approximately Japanese firms; namely, annual sales, number of employees, and number of business partners. First, new scaling relations including the distributions of fluctuations were discovered by systematically analyzing conditional statistics. Second, we introduced simple probabilistic models that reproduce all these scaling relations, and we derived relations between scaling exponents and the magnitude of fluctuations. [Copyright &y& Elsevier]