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.

The willingness of the middle and south of Iraq's population to accept different types of covid19 vaccines

Population around the world had huge concern toward COVID19 disease that affect directly or indirectly on all populations' lives. It caused by novel virus called sever acute respiratory syndrome coronavirus-2 (SARS-CoV-2), therefore;The Iraqi Ministry of Health (MOH) and numerous scientific agencies had advised to promote population for receiving Covid 19 vaccines as Largest astride of an attempt to reduce the serious challenges burden of Covidi 9 in global. Aims of Study are: 1-To evaluate prevalence of the willingness to accept different types of COVID19 vaccines in middle and south of Iraq. 2-Compare the vaccinated and unvaccinated of the southern Iraqi population and detection some factor behind acceptance of COVID19 vaccines. Method: Based Cross-Sectional Observational Study utilized validated questionnaire.it conducted from 5 February to 5 April 2022 and distribute in two ways,online and manually, depending on the different living locations of the participants. Results: The total number of participants' responses was 767. It is obtained from the distribution of surveys in different ways. 104 of participants were excluded due to not giving complete answers to the survey questionnaire. On the other hand, 42 of the participants were excluded due to not living in the middle and south of Iraq. 621 participants from the total participant response were enrolled in the study analysis. Half of participants were 50.08% female and other half were 49.92% male with 52.33% of the participants were married. About two-third of the participants accepted to receive covid19 vaccines (75.68%), while 24.31 % of the participants were unvaccinated(14.65% of them hesitant and 9.66% rejecters). Most of participants were from Basra city (79.87%) and more than half of them were vaccinated and other were unvaccinated (71.98% versus 28.02%) (P value=0.000). Two-third of the participants had graduated degree bachelor and more (75.8%) and the percent of vaccinated group of them were significantly higher than unvaccinated (80.9% versus 19.1%) (P value = 0.000). Less than half of the participants were the healthcare providers (35.91%) and the percent of vaccinated group of them were significantly higher than unvaccinated (82.96% versus 17.04%) (P value=0.002). A small percent of the participants had a chronic disease (15.46%) and 79.17% of them accepted to receive covid19 vaccines which was higher than unvaccinated (20.83%, where 9.3% hesitant and 11.45% rejecters). More than half of participants had sign and symptoms of Covid19 infection (61.84%) and 78.39% of them were vaccinated which was significantly higher than unvaccinated (21.61%) (P value=0.046) and only 41.22% of them were diagnosed by doctor. More than half of the participants had one of the family members and/or friends died from corona virus (66.51%) and 79.18% of them were vaccinated which was significantly higher than unvaccinated (20.82%) (P value=0.004). Conclusion From above results, the percent of covid 19 vaccines acceptance was higher than hesitancy and rejection. The higher rate of covid 19 vaccines acceptance could be related to many factors that might affect on the success of vaccinated programs, including: level of education, place of living, had sign and symptoms of Covid19 infection have family members and/or friends died from covid19 and are members of the health care providers. Knownledgment The study was supported by clinical trial

Pathophysiology study and sex differences response in COVID-19 patients

Coronavirus disease 2019 (COVID-19), an extremely infectious illness caused by a novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that has spread over the worldwide, has become one of the most difficult public health problems of our time. Age and gender are two major characteristics that influence the risks and outcomes of numerous diseases. Our study will investigate and compare the difference in hematological, biochemical, and serological biomarkers between sexes in order to evaluate severity and pathogenicity. Clinical records were taken from 150 SARS-CoV-2 positive patients were included in this study;the infection was confirmed by real time reverse transcriptase polymerase chain reaction. Blood samples subjected to measure changes in hematological parameters and serum subjected to measure biochemical test including ferritin, creatinine, CRP, D-dimer, and liver function enzyme either for ELISA test to measure serological biomarkers including IgM, IgG, TNF-α, IFN-γ, IL-6, and IL-10. 90 (60%) of whom were male and 60 (40%) of whom were female. Our study found a significant increase in CRP, IgM, IL-6, IL-10, TNF-α, IFN-γ, AST, ALP, and TBIL levels in males compared to females, and the age group most susceptible to SARS-CoV-2 infection was 41-60 years. Based on these findings, we concluded that males and those of older age had a high prevalence of severity and progression than females.

Beyond p < .05: a critical review of new Bayesian proposals for assessing the p-value.

This paper reviews recent contributions from a Bayesian-oriented perspective, after the ASA statement on p-values (2016). We classify proposals that (i) supplement the p-value; (ii) modify the p-value itself. In the first group, we review the Bayes factor, the False Positive risk, the rejection odds and the analysis of credibility from both Matthews' and Held's point of view. We also put forth and discuss a new index of credibility, about which we conduct a delimited simulation study. In the second group, we discuss Gannon's modification of the p-value based on the Bayes factor and the second-generation p-value. The theory is illustrated with two case studies on pharmacotherapy in infectious diseases. Contemporary authors still refer to the p-value as a statistical indicator but have abandoned the perspective of evaluating p-values with fixed thresholds. Statistical societies worldwide should target new strategies to disseminate the debate on p-values in all applied fields of knowledge, as well as they may promote the use of different statistical procedures to supplement p-values.

To curb research misreporting, replace significance and confidence by compatibility: A Preventive Medicine Golden Jubilee article.

It is well known that the statistical analyses in health-science and medical journals are frequently misleading or even wrong. Despite many decades of reform efforts by hundreds of scientists and statisticians, attempts to fix the problem by avoiding obvious error and encouraging good practice have not altered this basic situation. Statistical teaching and reporting remain mired in damaging yet editorially enforced jargon of "significance", "confidence", and imbalanced focus on null (no-effect or "nil") hypotheses, leading to flawed attempts to simplify descriptions of results in ordinary terms. A positive development amidst all this has been the introduction of interval estimates alongside or in place of significance tests and P-values, but intervals have been beset by similar misinterpretations. Attempts to remedy this situation by calling for replacement of traditional statistics with competitors (such as pure-likelihood or Bayesian methods) have had little impact. Thus, rather than ban or replace P-values or confidence intervals, we propose to replace traditional jargon with more accurate and modest ordinary-language labels that describe these statistics as measures of compatibility between data and hypotheses or models, which have long been in use in the statistical modeling literature. Such descriptions emphasize the full range of possibilities compatible with observations. Additionally, a simple transform of the P-value called the surprisal or S-value provides a sense of how much or how little information the data supply against those possibilities. We illustrate these reforms using some examples from a highly charged topic: trials of ivermectin treatment for Covid-19.

Misinterpretation of Statistical Nonsignificance as a Sign of Potential Bias: Hydroxychloroquine as a Case Study.

The term "statistical significance," ubiquitous in the medical literature, is often misinterpreted, as is the "p-value" from which it stems. This article explores the implications of results that are numerically positive (e.g., those in the treatment arm do better on average) but not statistically significant. This lack of statistical significance is sometimes interpreted as strong, even decisive, evidence against an effect without due consideration of other factors. Three influential articles on hydroxychloroquine (HCQ) as a treatment for COVID-19 are illustrative. They all involve numerically positive results that were not statistically significant that were misinterpreted as strong evidence against HCQ's efficacy. These and related considerations raise concerns regarding the reliability of academic/medical reasoning around COVID-19 treatments, as well as more generally, perhaps as a result of bias stemming from conflicts of interest.

An observational multi-centric COVID-19 sequelae study among health care workers.

Background: India has seen more than 43 million confirmed cases of COVID-19 as of April 2022, with a recovery rate of 98.8%, resulting in a large section of the population including the healthcare workers (HCWs), susceptible to develop post COVID sequelae. This study was carried out to assess the nature and prevalence of medical sequelae following COVID-19 infection, and risk factors, if any. Methods: This was an observational, multicenter cross-sectional study conducted at eight tertiary care centers. The consenting participants were HCWs between 12 and 52 weeks post discharge after COVID-19 infection. Data on demographics, medical history, clinical features of COVID-19 and various symptoms of COVID sequelae was collected through specific questionnaire. Finding: Mean age of the 679 eligible participants was 31.49 ± 9.54 years. The overall prevalence of COVID sequelae was 30.34%, with fatigue (11.5%) being the most common followed by insomnia (8.5%), difficulty in breathing during activity (6%) and pain in joints (5%). The odds of having any sequelae were significantly higher among participants who had moderate to severe COVID-19 (OR 6.51; 95% CI 3.46-12.23) and lower among males (OR 0.55; 95% CI 0.39-0.76). Besides these, other predictors for having sequelae were age (≥45 years), presence of any comorbidity (especially hypertension and asthma), category of HCW (non-doctors vs doctors) and hospitalisation due to COVID-19. Interpretation: Approximately one-third of the participants experienced COVID sequelae. Severity of COVID illness, female gender, advanced age, co-morbidity were significant risk factors for COVID sequelae. Funding: This work is a part of Indian Council for Medical Research (ICMR)- Rational Use of Medicines network. No additional financial support was received from ICMR to carry out the work, for study materials, medical writing, and APC.

Factors Affecting Chinese Economy During COVID-19 Based on Regression Model in SPSS

The epidemic has had a profound impact on Chinese economy. In order to explore which factors have played an important role in Chinese economic development, this paper uses Internet software such as SPSS to construct a regression model to process and analyze economic data and evaluate its impact. First, we use SPSS software to construct a regression equation model that affects China's economic development. After that, we select four new factors affecting China's economic development : personal consumption level, financial data governance level, big data support capabilities, and robotics capabilities. After that, we use the SLOPE regression function model to calculate the P value, the correlation coefficient beta and the variance var value, and derived the regression parameter equation from this. Finally, based on regression equations and regression coefficients, new suggestions for China's economic development are put forward. © 2021 IEEE.

Null-free False Discovery Rate Control Using Decoy Permutations.

The traditional approaches to false discovery rate (FDR) control in multiple hypothesis testing are usually based on the null distribution of a test statistic. However, all types of null distributions, including the theoretical, permutation-based and empirical ones, have some inherent drawbacks. For example, the theoretical null might fail because of improper assumptions on the sample distribution. Here, we propose a null distribution-free approach to FDR control for multiple hypothesis testing in the case-control study. This approach, named target-decoy procedure, simply builds on the ordering of tests by some statistic or score, the null distribution of which is not required to be known. Competitive decoy tests are constructed from permutations of original samples and are used to estimate the false target discoveries. We prove that this approach controls the FDR when the score function is symmetric and the scores are independent between different tests. Simulation demonstrates that it is more stable and powerful than two popular traditional approaches, even in the existence of dependency. Evaluation is also made on two real datasets, including an arabidopsis genomics dataset and a COVID-19 proteomics dataset.

Tze Leung Lai and Anna Choi's contribution to the discussion of 'Testing by betting: A strategy for statistical and scientific communication' by Glenn Shafer

Comments on an article by Glenn Shafer (see record 2021-44219-001). It is exciting to follow Glenn Shafer's investigations into forecasting, betting, reasoning with uncertainty and foundational issues in probability, beginning with his 1973 PhD thesis at Princeton and culminating in Shafer on the Dempster-Shafer theory of belief functions, and its evolution during the past five decades to the present paper on betting scores and game-theoretic probability. Betting scores are particularly relevant in this momentous year of intensive global search for COVID19 vaccines and treatments, and upcoming presidential and congressional elections in the United States, about which pundits keep giving time-varying forecasts of the outcomes while betting markets on presidential election odds have been particularly active, similar to online sports betting markets. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Increased risk of COVID-19 from walking dogs? Most likely, a spurious finding.

We analyse the paper "The spread of SARS-CoV-2 in Spain: Hygiene habits, sociodemographic profile, mobility patterns and comorbidities" authored by Rodríguez-Barranco et al. (2021), published in Environmental Research, vol.192, January 2021. The study was carried out under challenging conditions and provides original data of great value for exploratory purposes. Nevertheless, we found that the authors have not considered the potential effect of the multiple hypothesis testing carried out until obtaining the final model on the increased occurrence of false discoveries by mere chance. After adjusting the results provided in the paper for the effects of multiple testing, we conclude that only one of the five factors cited as statistically significant and relevant in the article, living with someone who has suffered from COVID-19, remained significantly related to the relative prevalence of COVID-19. Therefore, the preeminent role given in the analysed work to walking the dog as one of the main transmission routes of COVID-19 probably does not correspond to an actual effect. Instead, until replicated by other studies, it should be considered a spurious discovery.

Impact of the inversion and air pollution on the number of patients with Covid-19 in the metropolitan city of Tehran.

There is a downward curve between increasing inversion altitude and the number of coronavirus patients during all periods. As temperature inversion altitude increases, the pollutants are dispersed in a greater thickness of the atmosphere and the concentration of the pollutants decreases on the earth's surface. At the same time, the number of patients with Covid-19 reduces. Although investigation of the effect of severity of pollutants on the number of coronavirus patients showed poor significance level during the periods, a decreasing and increasing relationship was shown. in 1- and 9-14-day periods, the correlation coefficient was negative. As a result, the effect of the severity of pollutants and Covid-19 is not observed on 1- and9-14-day periods. Conversely, during2-8-day periods, a positive correlation coefficient was observed. Therefore, the time between infection with the virus and the onset of symptoms of this disease is between 2 and 8 days, in which the 3-day period showed the highest correlation. Considering the relationship between inversion altitude, the severity of pollutants and the number of patients during 2-5-day periods, it can be concluded that in the metropolitan city of Tehran, the maximum infection of this virus and the onset of symptoms is between 2 and 5 days.