Pub Date : 2024-02-27DOI: 10.11648/j.bsi.20240901.11
Betelihem Yirdaw, Y. Jibril, Ayelech Muluneh
Foot and mouth disease (FMD) is the most contagious disease of animals. The disease affects domestic cloven-hoofed animals, including cattle, swine, sheep, and goats, deer, and is characterized by fever, lameness, and vesicular lesions on the tongue, feet, snout, and teats. The study aimed to determine seroprevalence of FMD (Foot and Mouth Disease), to identifay type of serotypes and to know associated risk factors. A total of 389 sera samples were collected from cattle in four districts of the north western Amhara region and subjected to a 3ABC enzyme-linked immune-sorbent assay. The overall seroprevalence of FMDV was 5.66% (22/389); (95%; CI: 3.34% to 7.98%). The 22 positive samples were subjected to solid phase competitive ELISA to identify specific serotypes. The occurrence of FMD was higher in Adet (OR= 12.8), greater in the semi-intensive than extensive production systems (OR=10.4) and highly occurred in the cross breed than local breed cattle (OR=3.56). Serotypes identified in the four districts were type O, SAT2, and A. This study revealed that FMD is a prevalent and endemic disease. Thus, awareness creation to the stakeholders on the control and prevention of a disease is required. Further epidemiological investigation and vaccine trials should be conducted.
{"title":"Seroprevalence, Serotyping, and Associated Risk Factors of Foot and Mouth Diseases in Bovine in Western Amhara Regional State, North Western Ethiopia","authors":"Betelihem Yirdaw, Y. Jibril, Ayelech Muluneh","doi":"10.11648/j.bsi.20240901.11","DOIUrl":"https://doi.org/10.11648/j.bsi.20240901.11","url":null,"abstract":"Foot and mouth disease (FMD) is the most contagious disease of animals. The disease affects domestic cloven-hoofed animals, including cattle, swine, sheep, and goats, deer, and is characterized by fever, lameness, and vesicular lesions on the tongue, feet, snout, and teats. The study aimed to determine seroprevalence of FMD (Foot and Mouth Disease), to identifay type of serotypes and to know associated risk factors. A total of 389 sera samples were collected from cattle in four districts of the north western Amhara region and subjected to a 3ABC enzyme-linked immune-sorbent assay. The overall seroprevalence of FMDV was 5.66% (22/389); (95%; CI: 3.34% to 7.98%). The 22 positive samples were subjected to solid phase competitive ELISA to identify specific serotypes. The occurrence of FMD was higher in Adet (OR= 12.8), greater in the semi-intensive than extensive production systems (OR=10.4) and highly occurred in the cross breed than local breed cattle (OR=3.56). Serotypes identified in the four districts were type O, SAT2, and A. This study revealed that FMD is a prevalent and endemic disease. Thus, awareness creation to the stakeholders on the control and prevention of a disease is required. Further epidemiological investigation and vaccine trials should be conducted.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"28 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140426297","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 : 2023-07-31DOI: 10.11648/j.bsi.20230802.11
R. Karuppusami, Gomathi Sudhakar, Juliya Pearl Joseph Johnson, R. Mariappan, J. Rani, B. Antonisamy, Prasanna S. Premkumar
: In repeated measures data, the observations tend to be correlated within each subject, and such data are often analyzed using Generalized Estimating Equations (GEE), which are robust to assumptions that many methods hold. The main limitation of GEE is that its method of estimation is quasi-likelihood. The recent framework of the copula is very popular for handling repeated data. The maximum likelihood-based analysis for repeated data can be obtained using Gaussian copula regression. The purpose of this study is to show the handling and analysis of the repeated data using the Gaussian copula regression approach and compare the findings with GEE. The prospective, double-blinded, randomized controlled trial data for this study was obtained from the Department of Anesthesia, Christian Medical College
{"title":"A Gaussian Copula Regression Approach for Modelling Repeated Data in Medical Research","authors":"R. Karuppusami, Gomathi Sudhakar, Juliya Pearl Joseph Johnson, R. Mariappan, J. Rani, B. Antonisamy, Prasanna S. Premkumar","doi":"10.11648/j.bsi.20230802.11","DOIUrl":"https://doi.org/10.11648/j.bsi.20230802.11","url":null,"abstract":": In repeated measures data, the observations tend to be correlated within each subject, and such data are often analyzed using Generalized Estimating Equations (GEE), which are robust to assumptions that many methods hold. The main limitation of GEE is that its method of estimation is quasi-likelihood. The recent framework of the copula is very popular for handling repeated data. The maximum likelihood-based analysis for repeated data can be obtained using Gaussian copula regression. The purpose of this study is to show the handling and analysis of the repeated data using the Gaussian copula regression approach and compare the findings with GEE. The prospective, double-blinded, randomized controlled trial data for this study was obtained from the Department of Anesthesia, Christian Medical College","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132412583","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 : 2023-03-20DOI: 10.11648/j.bsi.20230801.13
Otieno Otieno, M. Kosgei, Nelson Onyango Owuor
: One of the dominant challenges affecting low and middle countries is the regard of child mortality. It had been a millennium development goal to reduce infant and child mortality by two-thirds in 1990 mortality levels by the year 2015. Therefore, the aspiration to recognize the causal factors of under five child mortality poses a crucial aspect of research. In principal, remarkable progress has been made in bringing down mortality in children under 5 years of age. The global under five mortality rate declined by 59 per cent, from 93 deaths per 1,000 live births in 1990 to 38 in 2019. In Kenya, the infant mortality rate in 2021 is 32.913 deaths per 1000 live births, a 3.36 per cent decline from the year 2020. It was 34.056 deaths per 1,000 live births in 2020, a decline of 3.24 per cent from the year 2019. In Nyanza Province, Kenya, has the highest infant mortality rate (133 deaths per 1,000 live births) while the lowest in Central Province (44 deaths per 1,000 live births). Despite all that improvement, the world is still doubtful to achieve that millennium development goal target number four, of diminishing child mortality. Our study aims to scrutinize on vital covariates affecting child mortality in Nyanza, Kenya. The principal purpose of this paper is to scrutinize the effect of demographic and socioeconomic variables on child mortality. We carried out a series of model evaluations to ascertain the best model under various scenarios bearing in mind the presence of dependencies due to Clusters and households. Then, performed a linear mixed effects model with the best fit based on data from Kenya Demographic and Health Survey (KDHS 2014) which was collected by use of questionnaires. Child mortality from the, KDHS 2014 data, was analyzed in an age period: mortality from the age of 12 months to the age of 60 months. The study reveals that, number of children under 5 in household, number of births in last 5 years, modern family planning and contraceptive use had an exceptional impact on child mortality.
{"title":"On Multi-Level Modeling of Child Mortality with Application to KDHS Data 2014","authors":"Otieno Otieno, M. Kosgei, Nelson Onyango Owuor","doi":"10.11648/j.bsi.20230801.13","DOIUrl":"https://doi.org/10.11648/j.bsi.20230801.13","url":null,"abstract":": One of the dominant challenges affecting low and middle countries is the regard of child mortality. It had been a millennium development goal to reduce infant and child mortality by two-thirds in 1990 mortality levels by the year 2015. Therefore, the aspiration to recognize the causal factors of under five child mortality poses a crucial aspect of research. In principal, remarkable progress has been made in bringing down mortality in children under 5 years of age. The global under five mortality rate declined by 59 per cent, from 93 deaths per 1,000 live births in 1990 to 38 in 2019. In Kenya, the infant mortality rate in 2021 is 32.913 deaths per 1000 live births, a 3.36 per cent decline from the year 2020. It was 34.056 deaths per 1,000 live births in 2020, a decline of 3.24 per cent from the year 2019. In Nyanza Province, Kenya, has the highest infant mortality rate (133 deaths per 1,000 live births) while the lowest in Central Province (44 deaths per 1,000 live births). Despite all that improvement, the world is still doubtful to achieve that millennium development goal target number four, of diminishing child mortality. Our study aims to scrutinize on vital covariates affecting child mortality in Nyanza, Kenya. The principal purpose of this paper is to scrutinize the effect of demographic and socioeconomic variables on child mortality. We carried out a series of model evaluations to ascertain the best model under various scenarios bearing in mind the presence of dependencies due to Clusters and households. Then, performed a linear mixed effects model with the best fit based on data from Kenya Demographic and Health Survey (KDHS 2014) which was collected by use of questionnaires. Child mortality from the, KDHS 2014 data, was analyzed in an age period: mortality from the age of 12 months to the age of 60 months. The study reveals that, number of children under 5 in household, number of births in last 5 years, modern family planning and contraceptive use had an exceptional impact on child mortality.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124546597","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 : 2023-02-09DOI: 10.11648/j.bsi.20230801.12
Otieno Otieno, M. Kosgei, Nelson Onyango Owuor
: One of the Millennium Development Goals is the reduction of infant and child mortality by two-thirds of 1990 mortality levels by 2015. Generally, significant progress has been made in reducing mortality in children under five years of age. The global under-five mortality rate declined by 59 per cent, from 93 deaths per 1,000 live births in 1990 to 38 in 2019. In Kenya, the infant mortality rate in 2021 is 32.913 deaths per 1000 live births, a 3.36 per cent decline from 2020. In 2020 it was 34.056 deaths per 1,000 live births, a drop of 3.24 per cent from the year 2019. In Kenya, Nyanza Province has the highest infant mortality rate (133 deaths per 1,000 live births) and the lowest in Central Province (44 deaths per 1,000 live births). Despite this advancement, the world still needs to achieve that Millennium Development Goal, target number four, of reducing child mortality. This study aims at identifying vital risk factors affecting child mortality in Kenya. The paper's main objective is to determine the effect of socioeconomic and demographic variables on child mortality in the presence of dependencies in clusters. We then did a logistic regression and tested the proportionality of the significant covariates. Then, performed a Stratified Cox regression model and, finally, a shared frailty model in survival analysis based on data from the Kenya Demographic and Health Survey (KDHS 2014), which was collected using questionnaires. Child mortality from the KDHS 2014 data was analyzed in an ageing period: mortality from the age of 12 months to the age of 60 months, referred to as "child mortality". The study reveals that clusters (households), maternal age at birth, preceding birth interval length and the number of births in the last five years significantly impacted child mortality.
{"title":"Statistical Modelling and Evaluation of Determinants of Child Mortality in Nyanza, Kenya","authors":"Otieno Otieno, M. Kosgei, Nelson Onyango Owuor","doi":"10.11648/j.bsi.20230801.12","DOIUrl":"https://doi.org/10.11648/j.bsi.20230801.12","url":null,"abstract":": One of the Millennium Development Goals is the reduction of infant and child mortality by two-thirds of 1990 mortality levels by 2015. Generally, significant progress has been made in reducing mortality in children under five years of age. The global under-five mortality rate declined by 59 per cent, from 93 deaths per 1,000 live births in 1990 to 38 in 2019. In Kenya, the infant mortality rate in 2021 is 32.913 deaths per 1000 live births, a 3.36 per cent decline from 2020. In 2020 it was 34.056 deaths per 1,000 live births, a drop of 3.24 per cent from the year 2019. In Kenya, Nyanza Province has the highest infant mortality rate (133 deaths per 1,000 live births) and the lowest in Central Province (44 deaths per 1,000 live births). Despite this advancement, the world still needs to achieve that Millennium Development Goal, target number four, of reducing child mortality. This study aims at identifying vital risk factors affecting child mortality in Kenya. The paper's main objective is to determine the effect of socioeconomic and demographic variables on child mortality in the presence of dependencies in clusters. We then did a logistic regression and tested the proportionality of the significant covariates. Then, performed a Stratified Cox regression model and, finally, a shared frailty model in survival analysis based on data from the Kenya Demographic and Health Survey (KDHS 2014), which was collected using questionnaires. Child mortality from the KDHS 2014 data was analyzed in an ageing period: mortality from the age of 12 months to the age of 60 months, referred to as \"child mortality\". The study reveals that clusters (households), maternal age at birth, preceding birth interval length and the number of births in the last five years significantly impacted child mortality.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131227580","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 : 2021-10-15DOI: 10.11648/J.BSI.20210604.11
G. Benisti, Avi Magid
By September 2021 the outbreak of the COVID-19 caused 228.19 million confirmed cases and 4.7 million deaths globally. Mortality measures are frequently used to estimate the severity of a pandemic. Among them is the Case Fatality Rate (CFR). Some mathematical models were developed to estimate the impact of specific factors on the disease’s mortality. These models were developed before the COVID-19 vaccines were administrated, and therefore did not consider the vaccines influence on COVID-19 fatality. Moreover, some other factors associated with COVID-19 mortality such as diabetes and cardiovascular mortality were not included in these models. This study offers a mathematical model with some potential predictors of COVID-19 CFR during the fourth pandemic wave caused by the Delta variant. To evaluate these predictors, demographic and clinical information for 10 highly populated developed countries was retrieved from a real-time available website. Demographic data included population density, percent of population above age 65, GDP per capita, and percent of smoking. Clinical data included diabetes prevalence, cardiovascular death rate, percent of fully vaccinated population, and CFR. Single linear regressions were conducted to assess the association of each potential predictor with CFR. A backward multiple linear regression was conducted to identify the most parsimonious combination of the independent variables of this study predicting CFR. The model developed in this study suggests that percent of population above age 65, and cardiovascular death rate have a positive effect on CFR, i.e., they are associated with increased COVID-19 fatality rate during the fourth wave. In addition, GDP per capita has a negative effect on CFR, i.e. – higher GDP per capita is associated with lower fatality rate during COVID-19 fourth wave. Moreover, single linear regressions show a strong negative association between percent of fully vaccinated people in each country and CFR. This model sheds light on several potential demographic and clinical factors which may predict CFR in highly populated developed countries during the emergence of the Delta variant. Vaccination in accordance with the recommendations is recommended to reduce COVID-19 mortality.
{"title":"Predictors of COVID-19 Case Fatality Rate in Highly Populated Developed Countries During the Emergence of the Delta Variant","authors":"G. Benisti, Avi Magid","doi":"10.11648/J.BSI.20210604.11","DOIUrl":"https://doi.org/10.11648/J.BSI.20210604.11","url":null,"abstract":"By September 2021 the outbreak of the COVID-19 caused 228.19 million confirmed cases and 4.7 million deaths globally. Mortality measures are frequently used to estimate the severity of a pandemic. Among them is the Case Fatality Rate (CFR). Some mathematical models were developed to estimate the impact of specific factors on the disease’s mortality. These models were developed before the COVID-19 vaccines were administrated, and therefore did not consider the vaccines influence on COVID-19 fatality. Moreover, some other factors associated with COVID-19 mortality such as diabetes and cardiovascular mortality were not included in these models. This study offers a mathematical model with some potential predictors of COVID-19 CFR during the fourth pandemic wave caused by the Delta variant. To evaluate these predictors, demographic and clinical information for 10 highly populated developed countries was retrieved from a real-time available website. Demographic data included population density, percent of population above age 65, GDP per capita, and percent of smoking. Clinical data included diabetes prevalence, cardiovascular death rate, percent of fully vaccinated population, and CFR. Single linear regressions were conducted to assess the association of each potential predictor with CFR. A backward multiple linear regression was conducted to identify the most parsimonious combination of the independent variables of this study predicting CFR. The model developed in this study suggests that percent of population above age 65, and cardiovascular death rate have a positive effect on CFR, i.e., they are associated with increased COVID-19 fatality rate during the fourth wave. In addition, GDP per capita has a negative effect on CFR, i.e. – higher GDP per capita is associated with lower fatality rate during COVID-19 fourth wave. Moreover, single linear regressions show a strong negative association between percent of fully vaccinated people in each country and CFR. This model sheds light on several potential demographic and clinical factors which may predict CFR in highly populated developed countries during the emergence of the Delta variant. Vaccination in accordance with the recommendations is recommended to reduce COVID-19 mortality.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129266347","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 : 2021-08-23DOI: 10.11648/J.BSI.20210603.13
Vinoth Raman, K. Karuppaiah, Subash Chandrabose Gandhi
The spread of HIV remains a huge investigation in this present environment. A Mathematical or Statistical model must be developed for estimating parameters related to the epidemic, the death rate of affected cells or the infectious viral production rate. Inability to carry out people evaluates their HIV status has led to widespread lack of correct and comprehensive data on HIV infection, while an individual first involved. Stochastic model measures the predicted point of threshold through discrete and continuous distribution attained by many researchers in last two decades. This paper develops a stochastic model for the time of HIV epidemic in a homosexual population. Expected time of incubation period derived through shock model approach. The fitting of information sets generated through simulation methods that the Alpha statistical distribution ought to be assumed because the epidemic distribution planned the time of stochastic model to search out HIV epidemics. To check the validity of analytical arguments and to explore the dynamics of disease above the epidemic threshold, this study concludes, the possible significance of the result is that transmit HIV in incubation stage is quicker as the intensity of the immune system is lower.
{"title":"Stochastic Approach for Witnessing the Incubation Period of a Patient","authors":"Vinoth Raman, K. Karuppaiah, Subash Chandrabose Gandhi","doi":"10.11648/J.BSI.20210603.13","DOIUrl":"https://doi.org/10.11648/J.BSI.20210603.13","url":null,"abstract":"The spread of HIV remains a huge investigation in this present environment. A Mathematical or Statistical model must be developed for estimating parameters related to the epidemic, the death rate of affected cells or the infectious viral production rate. Inability to carry out people evaluates their HIV status has led to widespread lack of correct and comprehensive data on HIV infection, while an individual first involved. Stochastic model measures the predicted point of threshold through discrete and continuous distribution attained by many researchers in last two decades. This paper develops a stochastic model for the time of HIV epidemic in a homosexual population. Expected time of incubation period derived through shock model approach. The fitting of information sets generated through simulation methods that the Alpha statistical distribution ought to be assumed because the epidemic distribution planned the time of stochastic model to search out HIV epidemics. To check the validity of analytical arguments and to explore the dynamics of disease above the epidemic threshold, this study concludes, the possible significance of the result is that transmit HIV in incubation stage is quicker as the intensity of the immune system is lower.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126630190","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 : 2021-08-23DOI: 10.11648/J.BSI.20210603.15
Somayeh Habibpour, G. Afroz, Mohsen Shokohi Yekta, M. Besharat, V. Farzad, M. Nakhshab
Background: the birth of a premature baby puts a lot of stress on the family. This stress is due to the lack of physical, emotional and psychological readiness of the parent to enter this premature baby and the family faces many needs, therefore, attention to the specific issues and needs of these families is considered. Objective: The purpose of the present study was to construct and investigate the factor structure and validation of the parent - infant interaction questionnaire in Behshahr. Method: After translation, design and approval of items by experts in the field of psychology and psychometrics, 222 mothers with infants over 2 months were selected by convenience sampling method and based on the criteria of Klein, moreover, this questionnaire was administered to them. Exploratory and confirmatory factor analysis were used to evaluate the construct validity. Result: According to the research findings, 3 factors and 19 items were identified. In addition, the confirmatory factor analysis confirmed the three - factor model of this questionnaire. To measure the reliability of the test, the KR-20 method was used, whose coefficients ranged from 0.610 to 0.803. Conclusion: The results indicate that the questionnaire has desirable psychometric properties and is a good tool for measuring mother - infant interaction.
{"title":"Investigation of Factor Structure and Validation of the Parent - Infant Interaction Questionnair","authors":"Somayeh Habibpour, G. Afroz, Mohsen Shokohi Yekta, M. Besharat, V. Farzad, M. Nakhshab","doi":"10.11648/J.BSI.20210603.15","DOIUrl":"https://doi.org/10.11648/J.BSI.20210603.15","url":null,"abstract":"Background: the birth of a premature baby puts a lot of stress on the family. This stress is due to the lack of physical, emotional and psychological readiness of the parent to enter this premature baby and the family faces many needs, therefore, attention to the specific issues and needs of these families is considered. Objective: The purpose of the present study was to construct and investigate the factor structure and validation of the parent - infant interaction questionnaire in Behshahr. Method: After translation, design and approval of items by experts in the field of psychology and psychometrics, 222 mothers with infants over 2 months were selected by convenience sampling method and based on the criteria of Klein, moreover, this questionnaire was administered to them. Exploratory and confirmatory factor analysis were used to evaluate the construct validity. Result: According to the research findings, 3 factors and 19 items were identified. In addition, the confirmatory factor analysis confirmed the three - factor model of this questionnaire. To measure the reliability of the test, the KR-20 method was used, whose coefficients ranged from 0.610 to 0.803. Conclusion: The results indicate that the questionnaire has desirable psychometric properties and is a good tool for measuring mother - infant interaction.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132566658","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 : 2021-07-19DOI: 10.11648/J.BSI.20210602.12
Yi Xu, Yeqian Liu
Kaplan-Meier estimate or proportional hazards regression is commonly used directly to estimate the effect of treatment on survival time in randomized clinical studies. However, such methods usually lead to biased estimate of treatment effect in non-randomized or observational studies because the treated and untreated groups cannot be compared directly due to potential systematical difference in baseline characteristics. Researchers have developed various methods for adjusting biased estimates by balancing out confounding covariates such as matching or stratification on propensity score, inverse probability treatment weighting. However, very few studies have compared the performance of these methods. In this paper, we conducted an intensive case study to compare the performance of various bias correction methods for non-randomized studies and applied these methods to the right-heart catheterization (RHC) study to investigate the impact of RHC on the survival time of critically ill patients in the intensive care unit. Our findings suggest that, after bias adjustment procedures, RHC was associated with increased mortality. The inverse probability treatment weighting outperforms other bias adjustment methods in terms of bias, mean-squared error of the hazard ratio estimators, type I error and power. In general, a combination of these bias adjustment methods could be applied to make the estimation of the treatment effect more efficient.
{"title":"Bias Adjustment Methods for Analysis of a Non-randomized Controlled Trials of Right Heart Catheterization for Patients in ICU","authors":"Yi Xu, Yeqian Liu","doi":"10.11648/J.BSI.20210602.12","DOIUrl":"https://doi.org/10.11648/J.BSI.20210602.12","url":null,"abstract":"Kaplan-Meier estimate or proportional hazards regression is commonly used directly to estimate the effect of treatment on survival time in randomized clinical studies. However, such methods usually lead to biased estimate of treatment effect in non-randomized or observational studies because the treated and untreated groups cannot be compared directly due to potential systematical difference in baseline characteristics. Researchers have developed various methods for adjusting biased estimates by balancing out confounding covariates such as matching or stratification on propensity score, inverse probability treatment weighting. However, very few studies have compared the performance of these methods. In this paper, we conducted an intensive case study to compare the performance of various bias correction methods for non-randomized studies and applied these methods to the right-heart catheterization (RHC) study to investigate the impact of RHC on the survival time of critically ill patients in the intensive care unit. Our findings suggest that, after bias adjustment procedures, RHC was associated with increased mortality. The inverse probability treatment weighting outperforms other bias adjustment methods in terms of bias, mean-squared error of the hazard ratio estimators, type I error and power. In general, a combination of these bias adjustment methods could be applied to make the estimation of the treatment effect more efficient.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116769363","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 : 2021-04-26DOI: 10.11648/J.BSI.20210602.11
P. Banerjee, A. Goswami, Shreya Bhunia, Sudipta Basu
Background: Liver works as one of the most versatile organs in the human body. But any kind of disturbance occurs in the liver may cause the liver disease. One of the most common liver infections is hepatitis C which is caused by the Hepatitis C Virus (HCV). It is well known that liver is the largest solid organ in the human body and also it is called the exocrine gland as it secretes bile into the intestine. Aim: The aim of this study is to evaluate the causal relationship of Bilirubin with each liver biomarker using the advanced regression techniques. Methods: We use two advanced regression techniques, namely Joint Generalized Linear Model (JGLM) and Generalized Additive Model (GAM). For model selection, we check the AIC value, GCV score and adjusted R–square as well as the different diagnostic plots like Q–Q plot, Residual vs. Fitted plot etc. are displayed. Results: Bilirubin, a human liver disease biomarker, is a brownish yellow substance found in bile and it is produced in the liver when the old red blood cells break down. The present study reveals that Bilirubin is positively associated (p-value<0.05) with Aspartate Aminotransferase (AST), Creatinine (CREA), Gamma-Glutamyl Transpeptidase (GGT), Protein (PROT), Alkaline Phosphatase (ALP)*Albumin (ALB) and marginally associated with Choline Esterase (CHE)* Cholesterol (CHOL) (p-value=0.0591). While it is negatively associated (p-value < 0.05) with Age, Sex, Alkaline Phosphatase (ALP), Alanine Aminotransferase (ALT), Choline Esterase (CHE), Cholesterol (CHOL), Albumin (ALB), Creatinine (CREA)*Gamma-Glutamyl Transpeptidase (GGT) under JGLM. Besides of that, Bilirubin is positively associated with AST, CREA, GGT, (CREA*GGT), (CHE*CHOL) whereas it is negatively associated with Sex, ALT, CHE, CHOL. Also, ALB is highly positively significant as a non–parametric smoothing term (p-value < 0.001) under GAM. Conclusion: Both the advanced regression models JGLM and GAM explain the association between Bilirubin with other liver diseases biomarker in case of Hepatitis C.
{"title":"Determination of Causal Relationship Between Bilirubin and Other Liver Biomarker in Case of Hepatitis C","authors":"P. Banerjee, A. Goswami, Shreya Bhunia, Sudipta Basu","doi":"10.11648/J.BSI.20210602.11","DOIUrl":"https://doi.org/10.11648/J.BSI.20210602.11","url":null,"abstract":"Background: Liver works as one of the most versatile organs in the human body. But any kind of disturbance occurs in the liver may cause the liver disease. One of the most common liver infections is hepatitis C which is caused by the Hepatitis C Virus (HCV). It is well known that liver is the largest solid organ in the human body and also it is called the exocrine gland as it secretes bile into the intestine. Aim: The aim of this study is to evaluate the causal relationship of Bilirubin with each liver biomarker using the advanced regression techniques. Methods: We use two advanced regression techniques, namely Joint Generalized Linear Model (JGLM) and Generalized Additive Model (GAM). For model selection, we check the AIC value, GCV score and adjusted R–square as well as the different diagnostic plots like Q–Q plot, Residual vs. Fitted plot etc. are displayed. Results: Bilirubin, a human liver disease biomarker, is a brownish yellow substance found in bile and it is produced in the liver when the old red blood cells break down. The present study reveals that Bilirubin is positively associated (p-value<0.05) with Aspartate Aminotransferase (AST), Creatinine (CREA), Gamma-Glutamyl Transpeptidase (GGT), Protein (PROT), Alkaline Phosphatase (ALP)*Albumin (ALB) and marginally associated with Choline Esterase (CHE)* Cholesterol (CHOL) (p-value=0.0591). While it is negatively associated (p-value < 0.05) with Age, Sex, Alkaline Phosphatase (ALP), Alanine Aminotransferase (ALT), Choline Esterase (CHE), Cholesterol (CHOL), Albumin (ALB), Creatinine (CREA)*Gamma-Glutamyl Transpeptidase (GGT) under JGLM. Besides of that, Bilirubin is positively associated with AST, CREA, GGT, (CREA*GGT), (CHE*CHOL) whereas it is negatively associated with Sex, ALT, CHE, CHOL. Also, ALB is highly positively significant as a non–parametric smoothing term (p-value < 0.001) under GAM. Conclusion: Both the advanced regression models JGLM and GAM explain the association between Bilirubin with other liver diseases biomarker in case of Hepatitis C.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127595848","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 : 2021-03-10DOI: 10.11648/J.BSI.20210601.13
Yeqian Liu, Junyu Chen
In oncology clinical trials, the exact time of event occurrence such as tumor progression is usually unknown but the time interval within which the event occurs is known. The determination of such survival time can be subject to measurement error and influenced by the timing of scheduled assessment. Ignoring interval-censored survival time could lead to serious estimation bias. In addition, a crucial characteristic of interval-censored data is how frequently the measurement interval is taken, which directly determine the efficiency of statistical inference. Therefore, it is highly desirable to find statistical methods that are robust to different assessment frequencies. We compare conventional imputation-based approach with non-parametric approaches to handle interval-censored survival data. We apply these approaches to both hypothesis test and the estimations of hazard and survival functions. Empirical performance of these methods are assessed through extensive simulation studies with various sample sizes. A phase III randomized clinical trial on metastatic colorectal cancer is analyzed by using conventional approaches and non-parametric interval-censored analysis approaches. Out findings suggest that the phase III colorectal cancer clinical trial failed to show a clinical benefit of adding bevacizumab (B) to standard chemotherapy (CT), and the proposed non-parametric interval-censored analysis approaches outperforms the conventional approach for routine applications to oncology clinical trials to analyze interval-censored survival data.
{"title":"Non-parametric Analysis of Interval-Censored Survival Data with Application to a Phase III Metastatic Colorectal Cancer Clinical Trial","authors":"Yeqian Liu, Junyu Chen","doi":"10.11648/J.BSI.20210601.13","DOIUrl":"https://doi.org/10.11648/J.BSI.20210601.13","url":null,"abstract":"In oncology clinical trials, the exact time of event occurrence such as tumor progression is usually unknown but the time interval within which the event occurs is known. The determination of such survival time can be subject to measurement error and influenced by the timing of scheduled assessment. Ignoring interval-censored survival time could lead to serious estimation bias. In addition, a crucial characteristic of interval-censored data is how frequently the measurement interval is taken, which directly determine the efficiency of statistical inference. Therefore, it is highly desirable to find statistical methods that are robust to different assessment frequencies. We compare conventional imputation-based approach with non-parametric approaches to handle interval-censored survival data. We apply these approaches to both hypothesis test and the estimations of hazard and survival functions. Empirical performance of these methods are assessed through extensive simulation studies with various sample sizes. A phase III randomized clinical trial on metastatic colorectal cancer is analyzed by using conventional approaches and non-parametric interval-censored analysis approaches. Out findings suggest that the phase III colorectal cancer clinical trial failed to show a clinical benefit of adding bevacizumab (B) to standard chemotherapy (CT), and the proposed non-parametric interval-censored analysis approaches outperforms the conventional approach for routine applications to oncology clinical trials to analyze interval-censored survival data.","PeriodicalId":219184,"journal":{"name":"Biomedical Statistics and Informatics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121252716","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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