Journal of biometrics & biostatistics最新文献

{"title":"Sample Size Charts for Spearman and Kendall Coefficients","authors":"Justin May, S. Looney","doi":"10.37421/2155-6180.2020.11.440","DOIUrl":"https://doi.org/10.37421/2155-6180.2020.11.440","url":null,"abstract":"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.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"11 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70046807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semiparametric Likelihood Estimation with Clayton-Oakes Model for Multivariate Current Status Data","authors":"","doi":"10.29011/jbsb-109.100009","DOIUrl":"https://doi.org/10.29011/jbsb-109.100009","url":null,"abstract":"","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84711869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytical Visual Methods to Describe Practice Patterns in a Newly Diagnosed Multiple Myeloma Non-Interventional Disease Registry","authors":"Lihua Yue, Siwen He, Jay Cao, Ying-Ying Lu, Ahmed H. YoussefAgha, Jane Jiu Lu, Liang Liu, S. Srinivasan","doi":"10.37421/2155-6180.2020.11.438","DOIUrl":"https://doi.org/10.37421/2155-6180.2020.11.438","url":null,"abstract":"Our Biometric team was tasked with implementing a primary objective of a newly diagnosed Multiple Myeloma registry to describe practice patterns of common first-line treatment regimens and subsequent therapeutic strategies. This manuscript describes analytical visual methods we used to understand and summarize a complex data structure. We aim to present these methods in a cohesive holistic manner which threads together materials published over time, each with focused narrower objectives, deriving from this primary objective. Methods described in detail elsewhere are briefly revisited here to provide that holistic perspective and to provide details on subsequent variants in newer applications. These have also been used in clinical publications. The coding and graphical display related details corresponding to our Sankey plot clinical publication, for which our methods are unpublished, will also be provided.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"11 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70046737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PROSPECTIVELY ESTIMATING THE AGE OF INITIATION OF E-CIGARETTES AMONG U.S. YOUTH: FINDINGS FROM THE POPULATION ASSESSMENT OF TOBACCO AND HEALTH (PATH) STUDY, 2013-2017.","authors":"Adriana Pérez,&nbsp;Meagan Bluestein,&nbsp;Baojiang Chen,&nbsp;Cheryl L Perry,&nbsp;Melissa B Harrell","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Context: </strong>There is a lack of research that prospectively estimates the age of initiation of electronic cigarette use in U.S. youth. Younger ages of initiation of tobacco product use are associated with greater exposure to nicotine, and recently e-cigarette use has been associated with subsequent cigarette initiation. This study sought to estimate the distribution of the age of first reporting of e-cigarette use outcomes among youth never e-cigarette users overall, by sex and by race/ethnicity, prospectively.</p><p><strong>Methods: </strong>Secondary analysis of the Population Assessment of Tobacco and Health (PATH) youth dataset (ages 12-17) across waves 1 (2013-2014), 2 (2014-2015), 3 (2015-2016), and 4 (2016-2017) were conducted. Four outcomes are presented, age of first report of: (i) susceptibility to use, (ii) ever use, (iii) past 30-day use, and (iv) \"fairly regular\" e-cigarette use. Each outcome was prospectively estimated using participant age when they entered the study and the number of weeks between the last report of never use and the first report of each outcome across waves. Weighted survival analyses for interval censoring accounting for the complex survey design were implemented.</p><p><strong>Results: </strong>Among youth non-susceptible to e-cigarettes, 50.2% became susceptible to e-cigarette use by age 18. There were no statistically significant differences in the age of first report of susceptibility to e-cigarette use by sex or by race/ethnicity in this nationally representative sample of U.S. youth. Among never users, 41.7%, 23.5% and 10.3% initiated ever, past 30-day and \"fairly regular\" e-cigarette use by the age of 18, respectively. Less than 10% initiated ever e-cigarette use between the ages of 18 and 21. Boys had a higher risk of first reporting ever, past 30-day and \"fairly regular\" e-cigarette use at earlier ages than girls. Non-Hispanic Blacks and Other racial/ethnic groups were less likely than Non-Hispanic Whites to initiate ever e-cigarette use at earlier ages, and there was no difference between Non-Hispanic Whites and Hispanics. Hispanic, Non-Hispanic Black and Other racial/ethnic youth were less likely to first report past 30-day use and \"fairly regular\" e-cigarette use at earlier ages than Non-Hispanic White youth.</p><p><strong>Conclusion: </strong>This paper provides information on specific ages of the first report of e-cigarette use behaviors by sex and by race/ethnicity that can be used to culturally tailor e-cigarette interventions on specific windows of opportunity before youth begin using e-cigarettes or escalating their use.</p>","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"11 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861570/pdf/nihms-1661052.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25341830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short Prognostic APP for Multiple Myeloma","authors":"S. Srinivasan, Lihua Yue","doi":"10.37421/2155-6180.2020.11.439","DOIUrl":"https://doi.org/10.37421/2155-6180.2020.11.439","url":null,"abstract":"We briefly describe methods pertaining to the development of a prognostic tool for Multiple Myeloma and direct readers to detail published clinical and methods manuscripts. This short communication provides a simpler combined version of nomograms for predicting early and late survival in the context of Multiple Myeloma.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"11 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70046797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the Male and Older People Susceptibility to Death from (COVID-19) Using Statistical Models","authors":"R. Mamlook, Zakaria Hashi, Tiba Zaki Abdulhameed, H. Bzizi","doi":"10.37421/2155-6180.2020.11.445","DOIUrl":"https://doi.org/10.37421/2155-6180.2020.11.445","url":null,"abstract":"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.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"11 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70047392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing and then Confirming a Hypothesis Based on a Chronology of Several Clinical Trials: A Bayesian Application to Pirfenidone Mortality Results","authors":"Zhengning Lin, D. Berry","doi":"10.37421/JBMBS.2020.11.441","DOIUrl":"https://doi.org/10.37421/JBMBS.2020.11.441","url":null,"abstract":"Abstract 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.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"46 46 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70054267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Kumaraswamy-Rani Distribution and Its Applications","authors":"Zitouni Mouna","doi":"10.37421/2155-6180.2020.11.436","DOIUrl":"https://doi.org/10.37421/2155-6180.2020.11.436","url":null,"abstract":"In this paper, a new distribution called the Kumaraswamy-Rani (KR) distribution, as a Special model from the class of Kumaraswamy Generalized (KW-G) distributions, is introduced. Its statistical properties are explored. Estimation parameters based on maximum likelihood are obtained. We have illustrated the performances of the proposed distribution by some simulation studies. Finally, the significance of the KR distribution in terms of modeling real data set has been highlighted before it has been compared to some one parameter lifetime distributions.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"11 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70046685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PROSPECTIVELY ESTIMATING THE AGE OF INITIATION OF E-CIGARETTES AMONG U.S. YOUTH: FINDINGS FROM THE POPULATION ASSESSMENT OF TOBACCO AND HEALTH (PATH) STUDY, 2013-2017.","authors":"Adriana Pérez, Meagan A. Bluestein, Baojiang Chen, C. Perry, M. Harrell","doi":"10.37421/2155-6180.2020.11.442","DOIUrl":"https://doi.org/10.37421/2155-6180.2020.11.442","url":null,"abstract":"Context There is a lack of research that prospectively estimates the age of initiation of electronic cigarette use in U.S. youth. Younger ages of initiation of tobacco product use are associated with greater exposure to nicotine, and recently e-cigarette use has been associated with subsequent cigarette initiation. This study sought to estimate the distribution of the age of first reporting of e-cigarette use outcomes among youth never e-cigarette users overall, by sex and by race/ethnicity, prospectively. Methods Secondary analysis of the Population Assessment of Tobacco and Health (PATH) youth dataset (ages 12-17) across waves 1 (2013-2014), 2 (2014-2015), 3 (2015-2016), and 4 (2016-2017) were conducted. Four outcomes are presented, age of first report of: (i) susceptibility to use, (ii) ever use, (iii) past 30-day use, and (iv) \"fairly regular\" e-cigarette use. Each outcome was prospectively estimated using participant age when they entered the study and the number of weeks between the last report of never use and the first report of each outcome across waves. Weighted survival analyses for interval censoring accounting for the complex survey design were implemented. Results Among youth non-susceptible to e-cigarettes, 50.2% became susceptible to e-cigarette use by age 18. There were no statistically significant differences in the age of first report of susceptibility to e-cigarette use by sex or by race/ethnicity in this nationally representative sample of U.S. youth. Among never users, 41.7%, 23.5% and 10.3% initiated ever, past 30-day and \"fairly regular\" e-cigarette use by the age of 18, respectively. Less than 10% initiated ever e-cigarette use between the ages of 18 and 21. Boys had a higher risk of first reporting ever, past 30-day and \"fairly regular\" e-cigarette use at earlier ages than girls. Non-Hispanic Blacks and Other racial/ethnic groups were less likely than Non-Hispanic Whites to initiate ever e-cigarette use at earlier ages, and there was no difference between Non-Hispanic Whites and Hispanics. Hispanic, Non-Hispanic Black and Other racial/ethnic youth were less likely to first report past 30-day use and \"fairly regular\" e-cigarette use at earlier ages than Non-Hispanic White youth. Conclusion This paper provides information on specific ages of the first report of e-cigarette use behaviors by sex and by race/ethnicity that can be used to culturally tailor e-cigarette interventions on specific windows of opportunity before youth begin using e-cigarettes or escalating their use.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"11 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70047344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Statistical Study to Identify the Risk Factors of Heart Attack","authors":"Zoha Fatima, I. B. Naqvi, S. Hanook","doi":"10.4172/2155-6180.1000431","DOIUrl":"https://doi.org/10.4172/2155-6180.1000431","url":null,"abstract":"A statistical study has been conducted to identify the risk factors of heart attack. The study design used in this research is an observational cross sectional. A semi structured questionnaire was designed and surveyed consisting of 25 questions which were filled from 246 patients from two hospitals ‘Gulab Devi’ and ‘Jinnah Hosptial’ Lahore, Pakistan. Respondents were asked questions regarding some of the possible reasons that may cause heart attack. Out of 246 patients, 123 were cases (people who had a heart attack) and remaining 123 were control (people who only had chest pain). We took 123 patients in each group because we needed comparison. Spss and R SOFTWARE were used to determine results of this research. By using univariate, bivariate and multivariate analysis it was observed that the significant factors from model are diabetes blood pressure, sweating, heart attack before, age, severity of pain, medication and pressure of the work.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"10 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41852398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}