Proper statistical analysis is the most important thing in clinical trials if a person wants to come to accurate conclusions and make smart decisions about the safety and effectiveness of new medical interventions. The utilization of the Study Data Tabulation Model (SDTM) and the Analysis Dataset Model (ADaM) is imperative in facilitating this process. The Study Data Tabulation Model (SDTM) is a universally accepted and standardized framework utilized to structure and display data obtained from clinical trials. The utilization of a consistent structure for data representation facilitates the seamless integration and analysis of data derived from various studies. The Study Data Tabulation Model (SDTM) categorizes data into various domains, including but not limited to demographics, adverse events, and laboratory measurements. Variables within each domain are defined and coded using specific controlled terminology, ensuring consistency across different studies. The implementation of a standardized data structure facilitates the accessibility, comprehension, and analysis of data for statisticians, thereby mitigating the potential for errors and augmenting the overall quality of the statistical analysis. In contrast, the Analysis Dataset Model (ADaM) serves as a complementary framework to SDTM, with its primary objective being the preparation of datasets specifically tailored for statistical analysis. The main focus of the study is to examine statistical Analysis in Clinical Trials Using the Study Data Tabulation Model (SDTM) and the Analysis Dataset Model (ADaM). In addition, the study also efficiency and Time-Saving and impact on Data Quality.
{"title":"Statistical Analysis in Clinical Trials Using the Study Data Tabulation Model (SDTM) and the Analysis Dataset Model (ADaM): Effects, Obstacles, and Solutions","authors":"Patel Sagar Kumar, Mukkala Srinivasa Reddy, Patel Rachna, Bolla Sandeep","doi":"10.23937/2469-5831/1510052","DOIUrl":"https://doi.org/10.23937/2469-5831/1510052","url":null,"abstract":"Proper statistical analysis is the most important thing in clinical trials if a person wants to come to accurate conclusions and make smart decisions about the safety and effectiveness of new medical interventions. The utilization of the Study Data Tabulation Model (SDTM) and the Analysis Dataset Model (ADaM) is imperative in facilitating this process. The Study Data Tabulation Model (SDTM) is a universally accepted and standardized framework utilized to structure and display data obtained from clinical trials. The utilization of a consistent structure for data representation facilitates the seamless integration and analysis of data derived from various studies. The Study Data Tabulation Model (SDTM) categorizes data into various domains, including but not limited to demographics, adverse events, and laboratory measurements. Variables within each domain are defined and coded using specific controlled terminology, ensuring consistency across different studies. The implementation of a standardized data structure facilitates the accessibility, comprehension, and analysis of data for statisticians, thereby mitigating the potential for errors and augmenting the overall quality of the statistical analysis. In contrast, the Analysis Dataset Model (ADaM) serves as a complementary framework to SDTM, with its primary objective being the preparation of datasets specifically tailored for statistical analysis. The main focus of the study is to examine statistical Analysis in Clinical Trials Using the Study Data Tabulation Model (SDTM) and the Analysis Dataset Model (ADaM). In addition, the study also efficiency and Time-Saving and impact on Data Quality.","PeriodicalId":91282,"journal":{"name":"International journal of clinical biostatistics and biometrics","volume":"86 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139131909","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 : 2023-06-30DOI: 10.23937/2469-5831/1510050
EB Nkemnole, OO Kuti
{"title":"Fitting Birth and Death Queuing Models using Maximum Likelihood Estimation with Application to COVID-19 Pandemic in Sub-Saharan Africa","authors":"EB Nkemnole, OO Kuti","doi":"10.23937/2469-5831/1510050","DOIUrl":"https://doi.org/10.23937/2469-5831/1510050","url":null,"abstract":"","PeriodicalId":91282,"journal":{"name":"International journal of clinical biostatistics and biometrics","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83994388","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 : 2023-01-01DOI: 10.23937/2469-5831/1510051
Patel Sagar Kumar
{"title":"Biostatistical Methodologies in Clinical Trials: An Overview of Recent Developments and Pitfalls","authors":"Patel Sagar Kumar","doi":"10.23937/2469-5831/1510051","DOIUrl":"https://doi.org/10.23937/2469-5831/1510051","url":null,"abstract":"","PeriodicalId":91282,"journal":{"name":"International journal of clinical biostatistics and biometrics","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89447402","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 : 2022-12-31DOI: 10.23937/2469-5831/1510049
Welborn Timothy A, Dhaliwal Satvinder S
{"title":"Hypothyroidism: A Small Clinical Trial Will Quickly Resolve the Combination Therapy Controversy","authors":"Welborn Timothy A, Dhaliwal Satvinder S","doi":"10.23937/2469-5831/1510049","DOIUrl":"https://doi.org/10.23937/2469-5831/1510049","url":null,"abstract":"","PeriodicalId":91282,"journal":{"name":"International journal of clinical biostatistics and biometrics","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90268200","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 : 2022-06-30DOI: 10.23937/2469-5831/1510044
Qiu Tian, Ma Zhi, Zhao Yong, Wang Wenling, Jiang Huimin, Wang Fengdi, Chen Yuelu, Han Ting-Li, Yang Yang, Wang Lianlian
{"title":"The Comparison of Family Function and its Related Factors in First-Child Infertile Women and Second-Child Infertile Women after 'Two-Child' Policy in China","authors":"Qiu Tian, Ma Zhi, Zhao Yong, Wang Wenling, Jiang Huimin, Wang Fengdi, Chen Yuelu, Han Ting-Li, Yang Yang, Wang Lianlian","doi":"10.23937/2469-5831/1510044","DOIUrl":"https://doi.org/10.23937/2469-5831/1510044","url":null,"abstract":"","PeriodicalId":91282,"journal":{"name":"International journal of clinical biostatistics and biometrics","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74759131","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 : 2022-01-12DOI: 10.23937/2469-5831/1510045
Sarkar Suvobrata, Akhtar Murtaza
{"title":"Triple Negative Breast Cancer Prevalence in Indian Patients over a Decade: A Systematic Review","authors":"Sarkar Suvobrata, Akhtar Murtaza","doi":"10.23937/2469-5831/1510045","DOIUrl":"https://doi.org/10.23937/2469-5831/1510045","url":null,"abstract":"","PeriodicalId":91282,"journal":{"name":"International journal of clinical biostatistics and biometrics","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76218248","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 New Modified Liu Ridge-Type Estimator for the Linear Regression Model: Simulation and Application","authors":"Oladapo Olasunkanmi J, Owolabi Abiola T, Idowu Janet I, Ayinde Kayode","doi":"10.23937/2469-5831/1510048","DOIUrl":"https://doi.org/10.23937/2469-5831/1510048","url":null,"abstract":"","PeriodicalId":91282,"journal":{"name":"International journal of clinical biostatistics and biometrics","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86560404","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 : 2021-12-31DOI: 10.23937/2469-5831/1510043
Pang Haitao, Hsu Chai-Wei, Mu Rongji, Zhou Shouhao
{"title":"An R Package Unified Dose Finding for Continuous and Ordinal Outcomes in Phase I Dose-Finding Trials","authors":"Pang Haitao, Hsu Chai-Wei, Mu Rongji, Zhou Shouhao","doi":"10.23937/2469-5831/1510043","DOIUrl":"https://doi.org/10.23937/2469-5831/1510043","url":null,"abstract":"","PeriodicalId":91282,"journal":{"name":"International journal of clinical biostatistics and biometrics","volume":"110 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90386951","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 : 2021-09-06DOI: 10.23937/2469-5831/1510042
D. Topuz, K. Kılıç
Objective: The aim of this study is to determine whether the potential toxic copper element values measured in soils (X1), vegetables (X2) and waters (X3) have an effect on the copper elements in the stomach and intestinal tissue (Yi) (ppm) of individuals in an area of approximately 2400 km2 covering the east of Erciyes strato volcano. Methods: We applied Diamond’s fuzzy least squares (FLS) method, which assumes that the deviation between the observed and the predicted values is due to the fuzziness of the coefficients. We calculated many uncertainties and errors during the calculation of the estimator of each coefficient of the model based on the minimum blur criteria. Results: The turbidity level of the model, which was created with an approach of h = 0.5 tolerance level, was calculated as Z(x) = 74104. Goodness of fit test criteria of fuzzy model were calculated with the mean squared error (Mean Squared Error, MSE = 47), the square root of the mean squared error (Root Mean Squared Error, RMSE = 22) and the coefficient of determination (R2 = 0.02). Conclusion: As a result of the calculations, statistically, rTissue-Soil = 0.5, rTissue-Vegetable = 0.3, rTissue-Vater = 0.1 levels were determined between the potential toxic copper elements in the soil, vegetables and water and the potential toxic copper element value in the stomach and intestinal tissue. Applications to determine whether there is a relationship between potential toxic copper elements related to the study area and potential toxic copper element value in stomach and intestinal tissue are discussed for the first time in this study.
{"title":"The Modeling by Fuzzy Least Squares Regression Approach Relationships between Copper Values in the Soil, Vegetables, Fruits and Human Tissue","authors":"D. Topuz, K. Kılıç","doi":"10.23937/2469-5831/1510042","DOIUrl":"https://doi.org/10.23937/2469-5831/1510042","url":null,"abstract":"Objective: The aim of this study is to determine whether the potential toxic copper element values measured in soils (X1), vegetables (X2) and waters (X3) have an effect on the copper elements in the stomach and intestinal tissue (Yi) (ppm) of individuals in an area of approximately 2400 km2 covering the east of Erciyes strato volcano. Methods: We applied Diamond’s fuzzy least squares (FLS) method, which assumes that the deviation between the observed and the predicted values is due to the fuzziness of the coefficients. We calculated many uncertainties and errors during the calculation of the estimator of each coefficient of the model based on the minimum blur criteria. Results: The turbidity level of the model, which was created with an approach of h = 0.5 tolerance level, was calculated as Z(x) = 74104. Goodness of fit test criteria of fuzzy model were calculated with the mean squared error (Mean Squared Error, MSE = 47), the square root of the mean squared error (Root Mean Squared Error, RMSE = 22) and the coefficient of determination (R2 = 0.02). Conclusion: As a result of the calculations, statistically, rTissue-Soil = 0.5, rTissue-Vegetable = 0.3, rTissue-Vater = 0.1 levels were determined between the potential toxic copper elements in the soil, vegetables and water and the potential toxic copper element value in the stomach and intestinal tissue. Applications to determine whether there is a relationship between potential toxic copper elements related to the study area and potential toxic copper element value in stomach and intestinal tissue are discussed for the first time in this study.","PeriodicalId":91282,"journal":{"name":"International journal of clinical biostatistics and biometrics","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80863521","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 : 2021-08-16DOI: 10.23937/2469-5831/1510041
Saka Adisa Jamiu
Human heart is a strong muscle that pumps blood to the body. A normal, healthy adult heart is about the size of human clenched fist and it is like an engine that makes a car works, moves and functions properly as such, the heart keeps the body running. A healthy heart supplies the body with just the right amount of blood at the right rate for whatever the body is doing at that time. The flow of blood to the heart could be reduced by plaque build-up or blockage by a plaque suddenly ruptures, these could cause angina (chest pain or discomfort) or a heart attack. When the heart muscle does not get enough oxygen and blood nutrients, the heart muscle cells will die (heart attack) and weaken the heart, diminishing its ability to pump blood to the rest of the body. Remark that, if one is observing some unusual feeling in the heart, chest or in some related areas, this could result to carrying out an Electrocardiogram (ECG) test or examination. In this study, six factors (Sex, Weight, Height, Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP) and Heart Rate (HR)) were considered and investigated using factorial experiments to see those main effect(s) and interaction effect(s) that are significant in determining the activities of PR-interval in Electrocardiogram examination. The effect of Sex is the most significant of all the treatment effects considered, followed by the effects of Height and HR, while other factors were shown to have very little effect. Hence, it could be concluded that Sex, Height and HR are the most important factors influencing PR Interval in the ECG examination in order to evaluate the metabolic disorders, effects and side effects of pharmacotherapy, and the evaluation of primary and secondary cardiomyopathic processes, among others.
{"title":"Determination of Factors Influencing the Activities of PR Interval on the Electrocardiogram (ECG) in an Experiment of the Factorial Type","authors":"Saka Adisa Jamiu","doi":"10.23937/2469-5831/1510041","DOIUrl":"https://doi.org/10.23937/2469-5831/1510041","url":null,"abstract":"Human heart is a strong muscle that pumps blood to the body. A normal, healthy adult heart is about the size of human clenched fist and it is like an engine that makes a car works, moves and functions properly as such, the heart keeps the body running. A healthy heart supplies the body with just the right amount of blood at the right rate for whatever the body is doing at that time. The flow of blood to the heart could be reduced by plaque build-up or blockage by a plaque suddenly ruptures, these could cause angina (chest pain or discomfort) or a heart attack. When the heart muscle does not get enough oxygen and blood nutrients, the heart muscle cells will die (heart attack) and weaken the heart, diminishing its ability to pump blood to the rest of the body. Remark that, if one is observing some unusual feeling in the heart, chest or in some related areas, this could result to carrying out an Electrocardiogram (ECG) test or examination. In this study, six factors (Sex, Weight, Height, Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP) and Heart Rate (HR)) were considered and investigated using factorial experiments to see those main effect(s) and interaction effect(s) that are significant in determining the activities of PR-interval in Electrocardiogram examination. The effect of Sex is the most significant of all the treatment effects considered, followed by the effects of Height and HR, while other factors were shown to have very little effect. Hence, it could be concluded that Sex, Height and HR are the most important factors influencing PR Interval in the ECG examination in order to evaluate the metabolic disorders, effects and side effects of pharmacotherapy, and the evaluation of primary and secondary cardiomyopathic processes, among others.","PeriodicalId":91282,"journal":{"name":"International journal of clinical biostatistics and biometrics","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82290750","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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