Journal de biométrie et biostatistique

Introduction: Coronavirus disease 2019 (COVID-19) is one of the serious infectious diseases that is caused by a specific virus called syndrome coronavirus 2 viruses (SARSCoV-2). The rapid spread of COVID19 raises serious concerns about the globally growing death rate. Currently, cases are doubled in one week around the world. Recorded data shows that COVID-19 does not infect all patients equally. This opportunistic virus can affect people of any age and gender. Information about the reason for high mortality in the age group 60 and older is limited. The gender differences among all deceased are poorly known. To understand more about COVID-19, this study aims to examine the different age groups among the death and focuses on comparing genders between males and females.

Method: Statistical analysis including Pearson’s Chi-squared (χ2) and binary logistic regression was conducted based on existing data to examine factors relating to death, such as age and gender. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for death.

Results: The results show that males were 2.51 more likely to die of coronavirus COVID-19 than females. Moreover, the study found a significant increase in death for patients age 60 and older compared to patients age less than 40. Thus, males of 80+ age were found to be highly associated with death.

Conclusions: Older people and male are more susceptible to death from COVID-19,we should pay more attention to the elderly people and male with COVID-19. This imposes providing careful health care for this population.

Background: Designing a study for independent confirmation of a treatment effect is sometimes not practical due to required large sample size. Post hoc pooling of studies including those for learning purposes is subject to selection bias and therefore not scientifically solid. We propose a Bayesian approach which calibrates the role of prior information from historical studies for learning and confirming purposes. The method is illustrated in the analysis of mortality data for the pirfenidone NDA.

Methods: The pirfenidone NDA includes three placebo-controlled studies to demonstrate efficacy for idiopathic pulmonary fibrosis (IPF), a rare and ultimately fatal lung disease with no approved treatment in the US at the time of NDA. The results of two earlier conducted studies PIPF-004 and PIPF-006 suggested that pirfenidone might reduce mortality risk. We used a Bayesian analysis to synthesize mortality results from the subsequent confirmative Study PIPF-016 and the combination of Studies PIPF-004 and PIPF-006.

Results: Pirfenidone’s treatment effect on mortality rate reduction for Study PIPF-016 is statistically significant with discounts of historical evidence from PIPF-044 and PIPF-006 for both all-cause mortality and treatment-emergent IPF-related mortality.

Conclusions: The Bayesian analysis provides a formal method to calibrate the role of information from historical evidence in the overall interpretation of results from both historical and concurrent clinical studies. The increased efficiency of using all available data is especially important in drug development for rare diseases with serious consequences, where limited patient source prohibits large trials, and unmet medical needs demand rapid access to treatment options.

Bivariate correlation analysis is one of the most commonly used statistical methods. Unfortunately, it is generally the case that little or no attention is given to sample size determination when planning a study in which correlation analysis will be used. For example, our review of clinical research journals indicated that none of the 111 articles published in 2014 that presented correlation results provided a justification for the sample size used in the correlation analysis. There are a number of easily accessible tools that can be used to determine the required sample size for inference based on a Pearson correlation coefficient; however, we were unable to locate any widely available tools that can be used for sample size calculations for a Spearman correlation coefficient or a Kendall coefficient of concordance. In this article, we provide formulas and charts that can be used to determine the required sample size for inference based on either of these coefficients. Additional sample size charts are provided in the Supplementary Materials.