Search method for two cutoff values in clinical trial using Kruskal-Wallis test by minimum P-value approach

In clinical trials, age is often converted to binary data by the cutoff value. However, when looking at a scatter plot for a group of patients whose age is larger than or equal to the cutoff value, age and outcome may not be related. If the group whose age is greater than or equal to the cutoff value is further divided into two groups, the older of the two groups may appear to be at lower risk. In this case, it may be necessary to further divide the group of patients whose age is greater than or equal to the cutoff value into two groups. This study provides a method for determining which of the two or three groups is the best split. The following two methods are used to divide the data. The existing method, the Wilcoxon-Mann-Whitney test by minimum P-value approach, divides data into two groups by one cutoff value. A new method, the Kruskal-Wallis test by minimum P-value approach, divides data into three groups by two cutoff values. Of the two tests, the one with the smaller P-value is used. Because this was a new decision procedure, it was tested using Monte Carlo simulations (MCSs) before application to the available COVID-19 data. The MCS results showed that this method performs well. In the COVID-19 data, it was optimal to divide into three groups by two cutoff values of 60 and 70 years old. By looking at COVID-19 data separated into three groups according to the two cutoff values, it was confirmed that each group had different features. We provided the R code that can be used to replicate the results of this manuscript. Another practical example can be performed by replacing x and y with appropriate ones.

A Characteristic Analysis and Countermeasure Study of the Hedging of Listed Companies in China Stock Markets

Due to COVID-19, the risk of price volatility in commodity and equity markets increases. The research and application of hedging is the most effective way to reduce the market risk. Hedging is a risk management strategy employed to offset losses in investments by taking an opposite position in a related asset. We use K-means and hierarchical clustering methods to cluster companies and futures products respectively, and analyze the relationship between the number of hedging firms, regional distribution, nature of firms, capital distribution, company size, profitability, number of local Futures Commission Merchants (FCMs), regional location, and listing time. The study shows that listed companies with large scale and good profitability invest more money in hedging, while state-owned enterprises' participation in hedging is more likely to be affected by the company size and the number of local futures commission merchants, and private enterprises are more likely to be affected by the company profitability and the regional location. Listed companies are more willing to choose long-listed and mature futures products for hedging. We also provide policy advice based on our conclusion. So far, there is no study on the characteristics of hedging. This paper fills the gap. The results provide a basis and guidance for people's investment and risk management. Using clustering analysis in hedging study is another innovation of this paper.