Pub Date : 2025-01-01Epub Date: 2025-09-10DOI: 10.23937/2469-5831/1510058
Alexandra R Brown, Byron J Gajewski, Matthew S Mayo, Edward F Ellerbeck, Christie A Befort
Background: Considerations must be taken when designing group-randomized trials due to the hierarchical structure of the data. Longitudinal group-randomized trials have an added layer of nesting adding more complexity to the study design. Simulation studies have been performed to compare the operating characteristics and validate statistical models for these hierarchical data structures, but many provide simulations from parametric distributions under set assumptions.
Methods: Our manuscript aims to use previous study data to compare two statistical analysis methods in group-randomized trial designs through data-driven simulations for a prospective study design. Creating simulated datasets using existing study data from a previous study allows the existing data to drive the assumptions of the models. The motivation for this simulation study was a potential concern that our proposed longitudinal mixed-effects model could have inflated type I error. We compare the empirical power and type I error rate for our proposed model against a baseline adjusted model at a single time point when modeling a continuous outcome, % weight change at 24 months. The longitudinal model includes three follow-up time points, while the other models the outcome with an adjustment for a baseline measure, weight. The empirical power of the models is calculated and compared for varying effect sizes.
Results: Results showed that the models had comparable power for the tested effect sizes and type I error rates of 3.09% and 3.87% for the longitudinal and the baseline adjusted model, respectively.
Conclusion: These results show our proposed longitudinal model does not result in an inflated type I error rate and would be sufficient to use for the future trial.
{"title":"Using Previous Longitudinal Group-Randomized Rural Weight-Loss Study Data to Design a Prospective Rural Weight-Loss Trial.","authors":"Alexandra R Brown, Byron J Gajewski, Matthew S Mayo, Edward F Ellerbeck, Christie A Befort","doi":"10.23937/2469-5831/1510058","DOIUrl":"10.23937/2469-5831/1510058","url":null,"abstract":"<p><strong>Background: </strong>Considerations must be taken when designing group-randomized trials due to the hierarchical structure of the data. Longitudinal group-randomized trials have an added layer of nesting adding more complexity to the study design. Simulation studies have been performed to compare the operating characteristics and validate statistical models for these hierarchical data structures, but many provide simulations from parametric distributions under set assumptions.</p><p><strong>Methods: </strong>Our manuscript aims to use previous study data to compare two statistical analysis methods in group-randomized trial designs through data-driven simulations for a prospective study design. Creating simulated datasets using existing study data from a previous study allows the existing data to drive the assumptions of the models. The motivation for this simulation study was a potential concern that our proposed longitudinal mixed-effects model could have inflated type I error. We compare the empirical power and type I error rate for our proposed model against a baseline adjusted model at a single time point when modeling a continuous outcome, % weight change at 24 months. The longitudinal model includes three follow-up time points, while the other models the outcome with an adjustment for a baseline measure, weight. The empirical power of the models is calculated and compared for varying effect sizes.</p><p><strong>Results: </strong>Results showed that the models had comparable power for the tested effect sizes and type I error rates of 3.09% and 3.87% for the longitudinal and the baseline adjusted model, respectively.</p><p><strong>Conclusion: </strong>These results show our proposed longitudinal model does not result in an inflated type I error rate and would be sufficient to use for the future trial.</p>","PeriodicalId":91282,"journal":{"name":"International journal of clinical biostatistics and biometrics","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12448146/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115358","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}
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}
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