Pub Date : 2023-05-11DOI: 10.19080/bboaj.2022.11.555810
Homeira Faridnejad
{"title":"Physicochemical Transformation Effect on the Relaxivity Enhancement of Iron Oxide Nanoparticles in Nuclear Medicine and MRI Imaging","authors":"Homeira Faridnejad","doi":"10.19080/bboaj.2022.11.555810","DOIUrl":"https://doi.org/10.19080/bboaj.2022.11.555810","url":null,"abstract":"","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48180808","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}
Pub Date : 2022-10-01DOI: 10.19080/bboaj.2022.11.555801
Folefac D Atem, Meagan A Bluestein, Melissa B Harrell, Baojiang Chen, Sarah E Messiah, Arnold E Kuk, Kymberle L Sterling, Charles E Spells, Adriana Pérez
Context: Youth tobacco use remains a prominent United States public health issue with a high economic and health burden.
Method: We pooled never and ever users at youth's first wave of PATH participation (waves 1-3) to estimate age of initiation for hookah, e-cigarettes, cigarettes, traditional cigars, cigarillos, and smokeless tobacco prospectively (waves 2-4). Age of initiation of each tobacco product was estimated using weighted interval-censored survival analyses. Weighted interval censoring Cox-proportional hazards regression models were used to assess the association of ever use of the TP at the first wave of PATH participation, sex, and race/ethnicity on the age of initiation of ever use of each tobacco product. Sensitivity analyses were performed to understand the impact of the recalled age of initiation for the left-censored participants by replacing the recalled age of initiation with a uniform "6" years lower bound.
Results: The proportion of those who ever used each tobacco product at the first wave of PATH participation ranged from 1.8% for traditional cigars to 10.4% for cigarettes. There was a significant increase in ever use of each tobacco product after the age of 14, with e-cigarettes and cigarettes showing the highest cumulative incidence of initiation by age 21, while smokeless and cigarillos recorded the lowest cumulative incidence by age 21. The adjusted Cox models showed boys initiated at earlier ages for all of these tobacco products except for hookah, which showed no difference. Similarly, apart from ever use of hookah, non-Hispanic White youth were more likely to initiate each tobacco product at earlier ages compared to Hispanic, non-Hispanic Black, and non-Hispanic Other youth.
Conclusion: The increased sample size and the inclusion of ever users yielded greater precision for age of initiation of each tobacco product than analyses limited to never users at the first wave of PATH participation. These analyses can help elucidate population selection criteria for estimating the age of initiation of tobacco products.
{"title":"Precise Estimation for the Age of Initiation of Tobacco Use Among U.S. Youth: Finding from the Population Assessment of Tobacco and Health (PATH) Study, 2013-2017.","authors":"Folefac D Atem, Meagan A Bluestein, Melissa B Harrell, Baojiang Chen, Sarah E Messiah, Arnold E Kuk, Kymberle L Sterling, Charles E Spells, Adriana Pérez","doi":"10.19080/bboaj.2022.11.555801","DOIUrl":"https://doi.org/10.19080/bboaj.2022.11.555801","url":null,"abstract":"<p><strong>Context: </strong>Youth tobacco use remains a prominent United States public health issue with a high economic and health burden.</p><p><strong>Method: </strong>We pooled never and ever users at youth's first wave of PATH participation (waves 1-3) to estimate age of initiation for hookah, e-cigarettes, cigarettes, traditional cigars, cigarillos, and smokeless tobacco prospectively (waves 2-4). Age of initiation of each tobacco product was estimated using weighted interval-censored survival analyses. Weighted interval censoring Cox-proportional hazards regression models were used to assess the association of ever use of the TP at the first wave of PATH participation, sex, and race/ethnicity on the age of initiation of ever use of each tobacco product. Sensitivity analyses were performed to understand the impact of the recalled age of initiation for the left-censored participants by replacing the recalled age of initiation with a uniform \"6\" years lower bound.</p><p><strong>Results: </strong>The proportion of those who ever used each tobacco product at the first wave of PATH participation ranged from 1.8% for traditional cigars to 10.4% for cigarettes. There was a significant increase in ever use of each tobacco product after the age of 14, with e-cigarettes and cigarettes showing the highest cumulative incidence of initiation by age 21, while smokeless and cigarillos recorded the lowest cumulative incidence by age 21. The adjusted Cox models showed boys initiated at earlier ages for all of these tobacco products except for hookah, which showed no difference. Similarly, apart from ever use of hookah, non-Hispanic White youth were more likely to initiate each tobacco product at earlier ages compared to Hispanic, non-Hispanic Black, and non-Hispanic Other youth.</p><p><strong>Conclusion: </strong>The increased sample size and the inclusion of ever users yielded greater precision for age of initiation of each tobacco product than analyses limited to never users at the first wave of PATH participation. These analyses can help elucidate population selection criteria for estimating the age of initiation of tobacco products.</p>","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9912413/pdf/nihms-1867583.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10707565","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}
Pub Date : 2021-05-05DOI: 10.19080/bboaj.2021.10.555788
Jonathan J Shuster
It is hard to believe that mainstream meta-analysis, whose primary objective is to provide a summary estimate of effect size from a set of completed studies, has a major flaw. Yet because the mainstream treats weights and/or sample sizes as constants, rather than unequivocally seriously random variables, this is exactly the situation. Further, the mainstream random effects model does not permit association between weights and effect size, which if false, can lead to major bias. For the following scenarios, we provide a fix, relying on ratio estimation from cluster sampling, to produce simple and valid asymptotic methods for the following scenarios: Estimation of means or proportions, differences of means or proportion from randomized trials, estimation of relative risk from randomized trials, and repeated measures Bland-Altman studies aimed at replacing invasive by non-invasive measures. One horror story for mainstream methods saw a highly significant result in a major study become insignificant when we kept the study point estimates the same but universally cut the study standard errors by 30%. With over 1400 meta-analyses papers published per month in 2019, it is essential to use this paper as a springboard to mitigate this situation.
{"title":"Meta-Analysis 2020: A Dire Alert and a Fix","authors":"Jonathan J Shuster","doi":"10.19080/bboaj.2021.10.555788","DOIUrl":"https://doi.org/10.19080/bboaj.2021.10.555788","url":null,"abstract":"It is hard to believe that mainstream meta-analysis, whose primary objective is to provide a summary estimate of effect size from a set of completed studies, has a major flaw. Yet because the mainstream treats weights and/or sample sizes as constants, rather than unequivocally seriously random variables, this is exactly the situation. Further, the mainstream random effects model does not permit association between weights and effect size, which if false, can lead to major bias. For the following scenarios, we provide a fix, relying on ratio estimation from cluster sampling, to produce simple and valid asymptotic methods for the following scenarios: Estimation of means or proportions, differences of means or proportion from randomized trials, estimation of relative risk from randomized trials, and repeated measures Bland-Altman studies aimed at replacing invasive by non-invasive measures. One horror story for mainstream methods saw a highly significant result in a major study become insignificant when we kept the study point estimates the same but universally cut the study standard errors by 30%. With over 1400 meta-analyses papers published per month in 2019, it is essential to use this paper as a springboard to mitigate this situation.","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47492746","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}
Pub Date : 2021-02-02DOI: 10.19080/bboaj.2021.10.555786
D. Noel
Usually, quantitative data standardization and/or normalization procedures requested in biological and as well in biomedical data analysis with the purpose to infer about linear regression relationship between processed variables and/or conditions. Here, we embarked to understand performance of quantitative data transformation systems in terms of reducing data variability as well as assessing data distribution normality by a computational statistic approach. For this purpose, we performed several multivariate descriptive and analytical statistical tests. Even if results shown drastic reduction of data variability by applying presently data transformation procedures, it is noteworthy to underline the relative opposite attitude of Exponential (Expo) data standardization system in that sense. In addition although, results revealed variance homogeneity for data processed by both Maximum and Logarithm data transformation methods, it is noteworthy to underline a relative variance homogeneity with regard data submitted to Box-Cox, Z-score, Minimum-Maximum and Square Root data transformation methods. Further, findings exhibited high aptitude of Square Root, Box-Cox and Logarithm quantitative data standardization methods, in stabilizing processed data variability. Interestingly, results shown high performances of Logarithm and Box-Cox data standardization systems in term of adjusting data normal distribution. In addition, multiple comparison of mean by Turkey contrast test suggested the high performance in term of data normality with regard Box-Cox standardization method. In conclusion, even if our results revealed heterogenic performances of presently processed quantitative data transformation methods, it is noteworthy to underline the high performances of both Box-Cox and Logarithm methods
{"title":"Normality Assessment of Several Quantitative Data Transformation Procedures","authors":"D. Noel","doi":"10.19080/bboaj.2021.10.555786","DOIUrl":"https://doi.org/10.19080/bboaj.2021.10.555786","url":null,"abstract":"Usually, quantitative data standardization and/or normalization procedures requested in biological and as well in biomedical data analysis with the purpose to infer about linear regression relationship between processed variables and/or conditions. Here, we embarked to understand performance of quantitative data transformation systems in terms of reducing data variability as well as assessing data distribution normality by a computational statistic approach. For this purpose, we performed several multivariate descriptive and analytical statistical tests. Even if results shown drastic reduction of data variability by applying presently data transformation procedures, it is noteworthy to underline the relative opposite attitude of Exponential (Expo) data standardization system in that sense. In addition although, results revealed variance homogeneity for data processed by both Maximum and Logarithm data transformation methods, it is noteworthy to underline a relative variance homogeneity with regard data submitted to Box-Cox, Z-score, Minimum-Maximum and Square Root data transformation methods. Further, findings exhibited high aptitude of Square Root, Box-Cox and Logarithm quantitative data standardization methods, in stabilizing processed data variability. Interestingly, results shown high performances of Logarithm and Box-Cox data standardization systems in term of adjusting data normal distribution. In addition, multiple comparison of mean by Turkey contrast test suggested the high performance in term of data normality with regard Box-Cox standardization method. In conclusion, even if our results revealed heterogenic performances of presently processed quantitative data transformation methods, it is noteworthy to underline the high performances of both Box-Cox and Logarithm methods","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47772560","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}
Pub Date : 2019-04-17DOI: 10.19080/bboaj.2019.09.555765
Silvia Pacei
{"title":"Small Sample Bias Corrections for Entropy Inequality Measures","authors":"Silvia Pacei","doi":"10.19080/bboaj.2019.09.555765","DOIUrl":"https://doi.org/10.19080/bboaj.2019.09.555765","url":null,"abstract":"","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45667871","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}
In this paper the problem of determining the functional relationship between time and the concentration of a chemical substance is studied. An intervention drug is administered on the experimental unit from which the chemical substance (specimen) is measured. This drug is hypothesized to cause a change in the concentration level of the chemical substance a certain lag-time after the intervention. However, the concentration value could not be directly measured, but rather a surrogate response can be measured. In the time-course study, this surrogate response is measured using different electrodes which possess varied behaviors. To utilize these surrogate measurements arising from the different electrodes (sensors), a calibration study is undertaken which measures the surrogate response for the different electrodes at known concentration levels. Based on the time-course and calibration data sets, a statistical procedure to estimate the signal function and the lag-time is proposed. Simulation studies indicate that the proposed procedure is able to reasonably recover the signal function and the lag-time. The procedure is then applied to the real data sets obtained during an analytical chemistry experiment.
{"title":"Estimating Concentration Response Function and Change-Point using Time-Course and Calibration Data.","authors":"B Qiang, A Abdalla, S Morgan, P Hashemi, E Peña","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>In this paper the problem of determining the functional relationship between time and the concentration of a chemical substance is studied. An intervention drug is administered on the experimental unit from which the chemical substance (specimen) is measured. This drug is hypothesized to cause a change in the concentration level of the chemical substance a certain lag-time after the intervention. However, the concentration value could not be directly measured, but rather a surrogate response can be measured. In the time-course study, this surrogate response is measured using different electrodes which possess varied behaviors. To utilize these surrogate measurements arising from the different electrodes (sensors), a calibration study is undertaken which measures the surrogate response for the different electrodes at known concentration levels. Based on the time-course and calibration data sets, a statistical procedure to estimate the signal function and the lag-time is proposed. Simulation studies indicate that the proposed procedure is able to reasonably recover the signal function and the lag-time. The procedure is then applied to the real data sets obtained during an analytical chemistry experiment.</p>","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8189314/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39100973","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}
Pub Date : 2018-10-04DOI: 10.19080/bboaj.2018.08.555736
Xi Chen
In a properly defined statistical analysis, the basic requirements include a complete specification of the sample space and a statistical model used in inference. The two are considered as key components for a statistical analysis, if there is any fuzzy description in the key components, the statistical analysis lost its foundation. For a clinical trial with interim analysis, although many techniques to deal with the error inflation caused by the repeated significance tests have been developed, the specifications of the key components were not complete, the clinical trial with interim analysis needs a further refinement.
{"title":"Conditional Analysis and Unconditional Conclusion in Clinical Trial with Interim Analysis","authors":"Xi Chen","doi":"10.19080/bboaj.2018.08.555736","DOIUrl":"https://doi.org/10.19080/bboaj.2018.08.555736","url":null,"abstract":"In a properly defined statistical analysis, the basic requirements include a complete specification of the sample space and a statistical model used in inference. The two are considered as key components for a statistical analysis, if there is any fuzzy description in the key components, the statistical analysis lost its foundation. For a clinical trial with interim analysis, although many techniques to deal with the error inflation caused by the repeated significance tests have been developed, the specifications of the key components were not complete, the clinical trial with interim analysis needs a further refinement.","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47603055","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}
Pub Date : 2018-09-25DOI: 10.19080/BBOAJ.2018.08.555735
Feyza Gunay
{"title":"Different Parameter Estimation Methods for Exponential Geometric Distribution and Its Applications in Lifetime Data Analysis","authors":"Feyza Gunay","doi":"10.19080/BBOAJ.2018.08.555735","DOIUrl":"https://doi.org/10.19080/BBOAJ.2018.08.555735","url":null,"abstract":"","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42449788","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}
Pub Date : 2018-09-17DOI: 10.19080/bboaj.2018.08.555734
A. Doostmoradi
In this paper, we proposed a new distribution to lifetime data with two parameters, the proposed distribution have increasing, decreasing and unimodal failure rates function. Some mathematical properties of the new distribution, including hazard function, moments, Estimation of Reliability, distribution of the order statistics and observed information matrix were presented. To estimate the model parameters, the Maximum Likelihood Estimate (MLE) technique was utilized. Then, one real data set were applied to show the significance and flexibility of the new distribution.
{"title":"A New Distribution with two parameters to Lifetime Data","authors":"A. Doostmoradi","doi":"10.19080/bboaj.2018.08.555734","DOIUrl":"https://doi.org/10.19080/bboaj.2018.08.555734","url":null,"abstract":"In this paper, we proposed a new distribution to lifetime data with two parameters, the proposed distribution have increasing, decreasing and unimodal failure rates function. Some mathematical properties of the new distribution, including hazard function, moments, Estimation of Reliability, distribution of the order statistics and observed information matrix were presented. To estimate the model parameters, the Maximum Likelihood Estimate (MLE) technique was utilized. Then, one real data set were applied to show the significance and flexibility of the new distribution.","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44482115","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}
Pub Date : 2018-09-14DOI: 10.19080/bboaj.2018.08.555733
J. Shuster
In this short communication, we describe a state of the art accuracy detection (polygraph) method, available at Converus.com. It is estimated to detect truthfulness (falseness) with 88% (86%) probability respectfully. While lie detection methods are not admissible in adjudicating guilt in criminal cases, this technology can be used to provide important ancillary information to those trying to fairly resolve disputes. We shall provide three examples: i. Helping Human Resources Departments resolve sexual harassment complaints in the MeToo# era; ii. Screening jailhouse informants in criminal procedures; and iii. Assessing unanimous guilty verdicts in capital murder cases, where jury deliberations took a very long time to achieve. These three applications should be viewed as examples of collaborations amongst those involved in important adjudication, experts in accuracy detection, and applied statisticians and biostatisticians, Other applications are encouraged.
{"title":"Accuracy Detection: Applications Where Biostatisticians Can Contribute","authors":"J. Shuster","doi":"10.19080/bboaj.2018.08.555733","DOIUrl":"https://doi.org/10.19080/bboaj.2018.08.555733","url":null,"abstract":"In this short communication, we describe a state of the art accuracy detection (polygraph) method, available at Converus.com. It is estimated to detect truthfulness (falseness) with 88% (86%) probability respectfully. While lie detection methods are not admissible in adjudicating guilt in criminal cases, this technology can be used to provide important ancillary information to those trying to fairly resolve disputes. We shall provide three examples: i. Helping Human Resources Departments resolve sexual harassment complaints in the MeToo# era; ii. Screening jailhouse informants in criminal procedures; and iii. Assessing unanimous guilty verdicts in capital murder cases, where jury deliberations took a very long time to achieve. These three applications should be viewed as examples of collaborations amongst those involved in important adjudication, experts in accuracy detection, and applied statisticians and biostatisticians, Other applications are encouraged.","PeriodicalId":72412,"journal":{"name":"Biostatistics and biometrics open access journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49178845","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}
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