Introduction: A common way for computing test-retest reliability is Intra Class Correlation which was developed for continuous variables. But it widely used to assess test-retest reliability in questionnaires with Likert scales. Most of the time consecutive numbers regarded as option labels of a question. If the probability of choosing options be the same, using this method is logic, otherwise it is not. Therefore, in this study a modified estimator of ICC is proposed to improve the estimation of ICC for ordinal scale by using latent variable model.
Methods: In this method test-retest answers were considered as bivariate variables and cumulative Probit latent variable model was fitted. A simulation study with N=1500 replicates was conducted to compare the ICC estimations of Likert scale approach with a latent variable approach. Different sample sizes (n=20, 30) was generated with different correlation parameters. The simulations were repeated for questions with 3 and 5 options with different probability of selecting options of a question. After that the two approaches were run on Beck for suicidal ideation questionnaire.
Results: In general the difference between Likert scale approach and latent variable approach were higher in 3 question options compared to 5 and also by increasing sample size and correlation between bivariate data, Root Mean Square Errors and bias were decreased. Assuming different probabilities for options, there was a considerably difference between Root Mean Square Errors, bias and standard deviation of estimation of ICC in two models. Using latent variable approach resulted less bias, SD and Root Mean Square Errors especially in lower sample sizes.
Conclusion: Simulations showed when the probability of choosing options of a question are skewed, using this method reduced Root Mean Square Errors especially when the options are less. This method was affected more on standard deviation compare to bias of estimations.
{"title":"A Modification on Intra Class Correlation Estimation for Ordinal Scale Variable Using Latent Variable Model","authors":"Samira Chaibakhsh, Asma Pourhoseingholi","doi":"10.18502/jbe.v9i1.13972","DOIUrl":"https://doi.org/10.18502/jbe.v9i1.13972","url":null,"abstract":"Introduction: A common way for computing test-retest reliability is Intra Class Correlation which was developed for continuous variables. But it widely used to assess test-retest reliability in questionnaires with Likert scales. Most of the time consecutive numbers regarded as option labels of a question. If the probability of choosing options be the same, using this method is logic, otherwise it is not. Therefore, in this study a modified estimator of ICC is proposed to improve the estimation of ICC for ordinal scale by using latent variable model.
 Methods: In this method test-retest answers were considered as bivariate variables and cumulative Probit latent variable model was fitted. A simulation study with N=1500 replicates was conducted to compare the ICC estimations of Likert scale approach with a latent variable approach. Different sample sizes (n=20, 30) was generated with different correlation parameters. The simulations were repeated for questions with 3 and 5 options with different probability of selecting options of a question. After that the two approaches were run on Beck for suicidal ideation questionnaire.
 Results: In general the difference between Likert scale approach and latent variable approach were higher in 3 question options compared to 5 and also by increasing sample size and correlation between bivariate data, Root Mean Square Errors and bias were decreased. Assuming different probabilities for options, there was a considerably difference between Root Mean Square Errors, bias and standard deviation of estimation of ICC in two models. Using latent variable approach resulted less bias, SD and Root Mean Square Errors especially in lower sample sizes.
 Conclusion: Simulations showed when the probability of choosing options of a question are skewed, using this method reduced Root Mean Square Errors especially when the options are less. This method was affected more on standard deviation compare to bias of estimations.
","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135870787","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}
Introduction:The bagging (BG) and random forest (RF) are famous supervised statistical learning methods based on the classification and regression trees. The BG and RF can deal with different types of responses such as categorical, continuous, etc. There are curves, time series, functional data, or observations that are related to each other based on their domain in many statistical applications. The RF methods are extended to some cases for functional data as covariates or responses in many pieces of literature. Among them, random-splitting is used to summarize the functional data to the multiple related summary statistics such as average, etc.
Methods: This research article extends this method and introduces the mixed data BG (MD-BG) and RF (MD-RF) algorithm for multiple functional and non-functional, or mixed and hybrid data, covariates and it calculates the variable importance plot (VIP) for each covariate.
Results: The main differences between MD-BG and MD-RF are in choosing the covariates that in the first, all covariates remain in the model but the second uses a random sample of covariates. The MD-RF helps to unmask the most important parts of functional covariates and the most important non-functional covariates.
Conclusion: We apply our methods on the two datasets of DTI and Tecator and compare their performances for continuous and categorical responses with developed R package (“RSRF”) in the GitHub.
{"title":"Random-Splitting Random Forest with Multiple Mixed-Data Covariates","authors":"Mohammad Fayaz, Alireza Abadi, Soheila Khodakarim","doi":"10.18502/jbe.v9i1.13974","DOIUrl":"https://doi.org/10.18502/jbe.v9i1.13974","url":null,"abstract":"Introduction:The bagging (BG) and random forest (RF) are famous supervised statistical learning methods based on the classification and regression trees. The BG and RF can deal with different types of responses such as categorical, continuous, etc. There are curves, time series, functional data, or observations that are related to each other based on their domain in many statistical applications. The RF methods are extended to some cases for functional data as covariates or responses in many pieces of literature. Among them, random-splitting is used to summarize the functional data to the multiple related summary statistics such as average, etc.
 Methods: This research article extends this method and introduces the mixed data BG (MD-BG) and RF (MD-RF) algorithm for multiple functional and non-functional, or mixed and hybrid data, covariates and it calculates the variable importance plot (VIP) for each covariate.
 Results: The main differences between MD-BG and MD-RF are in choosing the covariates that in the first, all covariates remain in the model but the second uses a random sample of covariates. The MD-RF helps to unmask the most important parts of functional covariates and the most important non-functional covariates.
 Conclusion: We apply our methods on the two datasets of DTI and Tecator and compare their performances for continuous and categorical responses with developed R package (“RSRF”) in the GitHub.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813141","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}
Introduction: The identification of climate temperature-sensitive pathogens and infectious diseases is essential in addressing health risks resulting from global warming. Such research is especially crucial in regions where climate change may have a more significant impact like Russia. Recent studies have reasoned that the abundance of non-toxigenic V. cholerae is environmentally driven and can be part of early global warming signals for Russian territory. The aim of the study is to investigate the spatial-temporal trends and thermo-climatic sensitivity of non-toxigenic V. cholerae abundance in Russia.
Methods: This study employed Kulldorff’s space-time statistics to identify persistent clusters of the V. cholerae ctx- isolation and areas for exploring temperature-depended patterns of the vibrio distribution. Correlation analysis was used to identify regions with temperature-driven Vibrio abundance in water samples.
Results: The spatial analysis detected 16 persistent (7-8 year) clusters of V. cholerae ctx- across the study period 2005-2021. The number of clusters with RR >1 abandoning from the south to the north and the total number of persistent clusters (9) is greater in the period of 2014(5)-2021 compared with the period 2005-2013 (7). A distinct significant thermo-climatic effect on the abundance of V. cholerae ctx- in water basins was found in three Russian regions with temperate marine (the Kaliningrad region) and sharp continental climatic conditions (the Irkutsk region and the Republic of Sakha). The temperature and Vibrio prevalence trend curves are peaky (the Kaliningrad region and the Republic of Sakha) or bell-shaped (the Irkutsk region) changed and closely followed together.
Conclusion: The persistent clusters should become targeted areas to improve sanitation conditions. The study offers valuable outcomes to support simplified empirical evaluations of the potential hazards of vibrio abundance that might be useful locally for public health authorities and globally as a part of Russia's warning system of climate change effects.
{"title":"Detection of Space-Time Clusters and Ambient Temperature Effects on Non-Toxigenic Vibrio Cholerae in Russia from 2005 To 2021","authors":"Vadim Leonov","doi":"10.18502/jbe.v9i1.13978","DOIUrl":"https://doi.org/10.18502/jbe.v9i1.13978","url":null,"abstract":"Introduction: The identification of climate temperature-sensitive pathogens and infectious diseases is essential in addressing health risks resulting from global warming. Such research is especially crucial in regions where climate change may have a more significant impact like Russia. Recent studies have reasoned that the abundance of non-toxigenic V. cholerae is environmentally driven and can be part of early global warming signals for Russian territory. The aim of the study is to investigate the spatial-temporal trends and thermo-climatic sensitivity of non-toxigenic V. cholerae abundance in Russia.
 Methods: This study employed Kulldorff’s space-time statistics to identify persistent clusters of the V. cholerae ctx- isolation and areas for exploring temperature-depended patterns of the vibrio distribution. Correlation analysis was used to identify regions with temperature-driven Vibrio abundance in water samples.
 Results: The spatial analysis detected 16 persistent (7-8 year) clusters of V. cholerae ctx- across the study period 2005-2021. The number of clusters with RR >1 abandoning from the south to the north and the total number of persistent clusters (9) is greater in the period of 2014(5)-2021 compared with the period 2005-2013 (7). A distinct significant thermo-climatic effect on the abundance of V. cholerae ctx- in water basins was found in three Russian regions with temperate marine (the Kaliningrad region) and sharp continental climatic conditions (the Irkutsk region and the Republic of Sakha). The temperature and Vibrio prevalence trend curves are peaky (the Kaliningrad region and the Republic of Sakha) or bell-shaped (the Irkutsk region) changed and closely followed together.
 Conclusion: The persistent clusters should become targeted areas to improve sanitation conditions. The study offers valuable outcomes to support simplified empirical evaluations of the potential hazards of vibrio abundance that might be useful locally for public health authorities and globally as a part of Russia's warning system of climate change effects.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813938","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}
Ahmad Reza Baghestani, Farid Zayeri, Mojtaba Meshkat
Background: Survival rates are important to show the progress of the disease and the effect of treatments. The estimation of survival probabilities especially in presence of highly censored data is challenging. In this study, Fuzzy Product Limit Estimator (FPLE) is introduced to mitigate the challenge.
Methods: In a longitudinal study, data of 173 CRC patients were analyzed. To estimate survival probabilities, mean and median survival time, Fuzzy Product Limit Estimator (FPLE), a data-driven method, was applied to the data. It provides a smooth survival probability curve and the continuation of the survival curve is not a concern in the case while the largest observed time is censored.
Results: One-year survival rate for CRC patients was estimated to be 83% using FPLE and KM methods. The five-year survival rate was estimated to be 37% and 52% by the FPLE and KM methods, respectively. The largest observed time in data (71.96 months) was censored, so the survival rate after 71.96 months was not estimable by the KM method. But 10-year and 20-year survival rates were estimated by FPLE as 0.21 and 0.09. The mean (median) survival time was estimated 45.97 (65) and 82.69 (41.70) months by KM and FPLE methods, respectively.
Conclusion: In presence of highly censored survival data, the FPLE method provides acceptable estimates of CRC patients' survival rate. Also, the continuation of the survival curve was estimated after the largest observed time. The smaller estimates by the FPLE at 5-year could be considered as warning that the actual survival rate is lower than that reported by the KM method.
{"title":"The Geometric Generalized Birnbaum–Saunders model with long-Term Survivors","authors":"Ahmad Reza Baghestani, Farid Zayeri, Mojtaba Meshkat","doi":"10.18502/jbe.v9i1.13973","DOIUrl":"https://doi.org/10.18502/jbe.v9i1.13973","url":null,"abstract":"Background: Survival rates are important to show the progress of the disease and the effect of treatments. The estimation of survival probabilities especially in presence of highly censored data is challenging. In this study, Fuzzy Product Limit Estimator (FPLE) is introduced to mitigate the challenge.
 Methods: In a longitudinal study, data of 173 CRC patients were analyzed. To estimate survival probabilities, mean and median survival time, Fuzzy Product Limit Estimator (FPLE), a data-driven method, was applied to the data. It provides a smooth survival probability curve and the continuation of the survival curve is not a concern in the case while the largest observed time is censored.
 Results: One-year survival rate for CRC patients was estimated to be 83% using FPLE and KM methods. The five-year survival rate was estimated to be 37% and 52% by the FPLE and KM methods, respectively. The largest observed time in data (71.96 months) was censored, so the survival rate after 71.96 months was not estimable by the KM method. But 10-year and 20-year survival rates were estimated by FPLE as 0.21 and 0.09. The mean (median) survival time was estimated 45.97 (65) and 82.69 (41.70) months by KM and FPLE methods, respectively. 
 Conclusion: In presence of highly censored survival data, the FPLE method provides acceptable estimates of CRC patients' survival rate. Also, the continuation of the survival curve was estimated after the largest observed time. The smaller estimates by the FPLE at 5-year could be considered as warning that the actual survival rate is lower than that reported by the KM method.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135814120","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}
Introduction: Recurrent event data, as a generalization of survival data, are frequently observed in various areas of medical research, including sequential hospitalizations in patients with schizophrenia. As experiencing multiple relapses during schizophrenia can have many implications, such as self-harm or harm to others, loss of education or employment, or other adverse outcomes, identifying and determining the most critical factors related to relapses in this disorder is essential. This study aimed to utilize heuristic approaches for selecting predictor variables in the field of recurrent events with an application to schizophrenia disorder
Methods: A two-step algorithm was employed to apply a combination of two variable selection methods, recursive feature elimination (RFE) and genetic algorithm feature selection (GAFS), and four modeling techniques: Gradient boosting (GB), artificial neural network (ANN), random forest (RF), and support vector machine (SVM) to simulated recurrent event datasets.
Results: In most simulation scenarios, the results indicated that the combination of RFE and RF applied to the deviance residual (DR) outperforms the other methods. The RFE-RF-DR selected the following predictor variables: Number of children, age, marital status, and history of substance abuse.
Conclusion: Our findings revealed that the proposed machine learning-based model is a promising technique for selecting predictor variables associated with a recurrent outcome when analyzing multivariate time-toevent data with recurrent events.
{"title":"Variable Selection for Recurrent Events Using Heuristic Approaches: Identifying Informative Variables for Rehospitalization in Schizophrenia Patients","authors":"Mahya Arayeshgar, Leili Tapak, Sharareh Parami, Behnaz Alafchi","doi":"10.18502/jbe.v9i1.13979","DOIUrl":"https://doi.org/10.18502/jbe.v9i1.13979","url":null,"abstract":"Introduction: Recurrent event data, as a generalization of survival data, are frequently observed in various areas of medical research, including sequential hospitalizations in patients with schizophrenia. As experiencing multiple relapses during schizophrenia can have many implications, such as self-harm or harm to others, loss of education or employment, or other adverse outcomes, identifying and determining the most critical factors related to relapses in this disorder is essential. This study aimed to utilize heuristic approaches for selecting predictor variables in the field of recurrent events with an application to schizophrenia disorder
 Methods: A two-step algorithm was employed to apply a combination of two variable selection methods, recursive feature elimination (RFE) and genetic algorithm feature selection (GAFS), and four modeling techniques: Gradient boosting (GB), artificial neural network (ANN), random forest (RF), and support vector machine (SVM) to simulated recurrent event datasets.
 Results: In most simulation scenarios, the results indicated that the combination of RFE and RF applied to the deviance residual (DR) outperforms the other methods. The RFE-RF-DR selected the following predictor variables: Number of children, age, marital status, and history of substance abuse.
 Conclusion: Our findings revealed that the proposed machine learning-based model is a promising technique for selecting predictor variables associated with a recurrent outcome when analyzing multivariate time-toevent data with recurrent events.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135870554","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}
Dorsa Moharerzadeh Kurd, Ali Seidkhani-Nahal, A. Noori-Zadeh, Atiye Sheikhabbasi, F. Heydari, I. Pakzad, R. Pakzad
Introduction: Statins is a class of lipid-lowering drugs and our previous investigations showed that statins have antiviral effects and have a wound healing effect in the lung. This systematic review and meta-analysis aimed to evaluate the effects of statin therapy on mortality and clinical outcomes in COVID-19 patients. Methods: A comprehensive search was conducted in international databases, including MEDLINE, Scopus, Web of Science, and Embase from December 1, 2019 until January 26, 2022 without any restriction in language. The random-effects model was used to estimate the pooled odds ratio (OR). Results: The statin therapy overally was associated with decrease in odds of ventilation [pooled OR (95% CI): 0.85 (0.70 to 0.99)] and mortality [pooled OR (95% CI): 0.73 (0.66 to 0.81)] but had no effects on the ICU admission [pooled OR (95% CI): 0.93 (0.77 to 1.12)], oxygen therapy [pooled OR (95% CI): 0.85 (0.70 to 0.99)], recovery [pooled OR (95% CI): 1.85 (0.35 to 9.92)], kidney failure [pooled OR (95% CI): 1.01 (0.73 to 1.40)], hospitalization [pooled OR (95% CI): 1.45 (0.88 to 2.36)], asymptomatic disease [pooled OR (95% CI): 1.33 (0.24 to 7.44)], and ARDS [pooled OR (95% CI): 1.15 (0.88 to 1.49)]. Conclusion: The present meta-analysis showed that statin therapy was associated with a reduced risk of mortality and ventilation in patients with COVID-19 but had no effects on other clinical outcomes.
引言：他汀类药物是一类降脂药物，我们之前的研究表明他汀类药物具有抗病毒作用，对肺部有伤口愈合作用。这项系统综述和荟萃分析旨在评估他汀类药物治疗对新冠肺炎患者死亡率和临床结果的影响。方法：从2019年12月1日至2022年1月26日，在MEDLINE、Scopus、Web of Science和Embase等国际数据库中进行全面搜索，不受语言限制。随机效应模型用于估计合并优势比（OR）。结果：他汀类药物治疗总体上与通气几率[合并OR（95%CI）：0.85（0.70至0.99）]和死亡率[合并OR）：0.73（0.66至0.81）]的降低有关，但对ICU入院率[合并OR，肾衰竭[合并OR（95%CI）：1.01（0.73至1.40）]、住院[合并OR，和ARDS[合并OR（95%CI）:1.15（0.88-1.49）]。结论：本荟萃分析表明，他汀类药物治疗与新冠肺炎患者死亡率和通气风险降低相关，但对其他临床结果没有影响。
{"title":"Association of Statin Therapy on Clinical Outcomes in Covid-19 Patients: An Updated Systematic Review and Meta-Analysis on All Related Evidences","authors":"Dorsa Moharerzadeh Kurd, Ali Seidkhani-Nahal, A. Noori-Zadeh, Atiye Sheikhabbasi, F. Heydari, I. Pakzad, R. Pakzad","doi":"10.18502/jbe.v8i4.13354","DOIUrl":"https://doi.org/10.18502/jbe.v8i4.13354","url":null,"abstract":"Introduction: Statins is a class of lipid-lowering drugs and our previous investigations showed that statins have antiviral effects and have a wound healing effect in the lung. This systematic review and meta-analysis aimed to evaluate the effects of statin therapy on mortality and clinical outcomes in COVID-19 patients. \u0000Methods: A comprehensive search was conducted in international databases, including MEDLINE, Scopus, Web of Science, and Embase from December 1, 2019 until January 26, 2022 without any restriction in language. The random-effects model was used to estimate the pooled odds ratio (OR). \u0000Results: The statin therapy overally was associated with decrease in odds of ventilation [pooled OR (95% CI): 0.85 (0.70 to 0.99)] and mortality [pooled OR (95% CI): 0.73 (0.66 to 0.81)] but had no effects on the ICU admission [pooled OR (95% CI): 0.93 (0.77 to 1.12)], oxygen therapy [pooled OR (95% CI): 0.85 (0.70 to 0.99)], recovery [pooled OR (95% CI): 1.85 (0.35 to 9.92)], kidney failure [pooled OR (95% CI): 1.01 (0.73 to 1.40)], hospitalization [pooled OR (95% CI): 1.45 (0.88 to 2.36)], asymptomatic disease [pooled OR (95% CI): 1.33 (0.24 to 7.44)], and ARDS [pooled OR (95% CI): 1.15 (0.88 to 1.49)]. \u0000Conclusion: The present meta-analysis showed that statin therapy was associated with a reduced risk of mortality and ventilation in patients with COVID-19 but had no effects on other clinical outcomes.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43693782","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}
Z. Montazeri, S. Kheiri, Shahram Paydar, M. Sedehi
Introduction: Coagulation disorder is one of the major phenomena following the trauma which can deteriorate the condition of the patients. The aim of this study is to determine some factors predicting the incidence of coagulation disorder among acute trauma patients. Methods: The generalized estimation equations were used to determine the predictors of blood coagulation disorders in a sample of 736 people over 16 years of age with acute trauma in Shahid Rajaei Hospital in Shiraz. The response variable was converted based on PT, PTT, INR, and fibrinogen level criteria as a two-state variable (with/without coagulation disorder). In the data analysis, the correlation of the coagulation disorder was considered in the first and second stages. Results:The prevalence of coagulation disorders (mild, moderate and severe) was 19% in two stages and coagulation disorders (moderate and severe) was 7.5%. Motor vehicle accident was the most common cause of injury.The variables of blood sugar, diastolic blood pressure, pH, and sodium had a significant effect on coagulation disorders (mild, moderate, and severe). Moreover, blood phosphorus, age, and pupillary reflex had a significant effect on coagulation disorders (moderate and severe). Conclusion: Predictors of coagulation disorders (mild-moderate-severe) include blood sugar, diastolic blood pressure, pH, and sodium. Moreover, blood phosphorus, age, and pupil reflex are predictors of moderate and severe coagulopathy. this model that taking into account the exchangeable correlation of first- and second-stage coagulopathy had a better fit than the model ignoring this correlation.
{"title":"The Coagulopathy-Predicting Factors in Acute Trauma Patients Using the Generalized Estimation Equations Model","authors":"Z. Montazeri, S. Kheiri, Shahram Paydar, M. Sedehi","doi":"10.18502/jbe.v8i4.13351","DOIUrl":"https://doi.org/10.18502/jbe.v8i4.13351","url":null,"abstract":"Introduction: Coagulation disorder is one of the major phenomena following the trauma which can deteriorate the condition of the patients. The aim of this study is to determine some factors predicting the incidence of coagulation disorder among acute trauma patients. \u0000Methods: The generalized estimation equations were used to determine the predictors of blood coagulation disorders in a sample of 736 people over 16 years of age with acute trauma in Shahid Rajaei Hospital in Shiraz. The response variable was converted based on PT, PTT, INR, and fibrinogen level criteria as a two-state variable (with/without coagulation disorder). In the data analysis, the correlation of the coagulation disorder was considered in the first and second stages. \u0000Results:The prevalence of coagulation disorders (mild, moderate and severe) was 19% in two stages and coagulation disorders (moderate and severe) was 7.5%. Motor vehicle accident was the most common cause of injury.The variables of blood sugar, diastolic blood pressure, pH, and sodium had a significant effect on coagulation disorders (mild, moderate, and severe). Moreover, blood phosphorus, age, and pupillary reflex had a significant effect on coagulation disorders (moderate and severe). \u0000Conclusion: Predictors of coagulation disorders (mild-moderate-severe) include blood sugar, diastolic blood pressure, pH, and sodium. Moreover, blood phosphorus, age, and pupil reflex are predictors of moderate and severe coagulopathy. this model that taking into account the exchangeable correlation of first- and second-stage coagulopathy had a better fit than the model ignoring this correlation.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43509531","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}
Armin Naghipour, A. Moghimbeigi, J. Poorolajal, Abdolazim Sadighi-Pashaki
Introduction: Gastric cancer; Case-Control; Conditional logistic regression; Bayesian; Matching.Gastric cancer is one of the most common and deadly cancers in Iran. Gastric cancer is highly dependent on nutritional factors and geographical location. Therefore, the aim of this study was to evaluate the effect of nutritional factors on gastric cancer in Hamadan-Iran. Methods: This study was performed as a matched case-control study that each case had two controls that matched with cases in age (±5 years) and gender at Diagnostic and Treatment Center of Mahdieh in Hamedan, Iran. First and second control groups contain persons with and without family history of cancer, respectively. Information of nutritional, epidemiological and confounding variables were collected for 100 cases and 200 controls. Controls from hospital samples, friends and acquaintances of the case group were selected. Data were collected using a researcher-made questionnaire. Data were analyzed using conditional logistic regression by Bayesian method. Results: Findings showed that, compared with individuals in the case group with the family history group with factors hot food (OR=2.35, 0.95%CrI=(1.82,5.19)), black tea (OR=1.60, 0.95%CrI (1.44,1.72)) cigarettes (OR=2.13, 0.95%CrI=(1.68,2.96)), red meat (OR=4.28, 0.95%CrI=(3.11,8.37)), residence (OR=3.15, 0.95%CrI= (1.62,5.65)), fruit (OR=0.75, 0.95% CrI=(0.63,0.83)) and vegetables (OR=0.76, 0.95%CrI=(0.59,0.85)) there was a strong statistical correlation. The results were also valid for the second control group. Conclusion: The study showed that many controllable nutritional factors in Hamadan affect the incidence of gastric cancer. It is recommended that policymakers and managers inform the public about the risk factors and prevention of gastric cancer through the publication of brochures, television and newspapers.
{"title":"Determination of the Factors Affecting the Survival Rate of Patients with Lung Cancer Using Bayesian Model; Historical Cohort","authors":"Armin Naghipour, A. Moghimbeigi, J. Poorolajal, Abdolazim Sadighi-Pashaki","doi":"10.18502/jbe.v8i4.13349","DOIUrl":"https://doi.org/10.18502/jbe.v8i4.13349","url":null,"abstract":"Introduction: Gastric cancer; Case-Control; Conditional logistic regression; Bayesian; Matching.Gastric cancer is one of the most common and deadly cancers in Iran. Gastric cancer is highly dependent on nutritional factors and geographical location. Therefore, the aim of this study was to evaluate the effect of nutritional factors on gastric cancer in Hamadan-Iran. \u0000Methods: This study was performed as a matched case-control study that each case had two controls that matched with cases in age (±5 years) and gender at Diagnostic and Treatment Center of Mahdieh in Hamedan, Iran. First and second control groups contain persons with and without family history of cancer, respectively. Information of nutritional, epidemiological and confounding variables were collected for 100 cases and 200 controls. Controls from hospital samples, friends and acquaintances of the case group were selected. Data were collected using a researcher-made questionnaire. Data were analyzed using conditional logistic regression by Bayesian method. \u0000Results: Findings showed that, compared with individuals in the case group with the family history group with factors hot food (OR=2.35, 0.95%CrI=(1.82,5.19)), black tea (OR=1.60, 0.95%CrI (1.44,1.72)) cigarettes (OR=2.13, 0.95%CrI=(1.68,2.96)), red meat (OR=4.28, 0.95%CrI=(3.11,8.37)), residence (OR=3.15, 0.95%CrI= (1.62,5.65)), fruit (OR=0.75, 0.95% CrI=(0.63,0.83)) and vegetables (OR=0.76, 0.95%CrI=(0.59,0.85)) there was a strong statistical correlation. The results were also valid for the second control group. \u0000Conclusion: The study showed that many controllable nutritional factors in Hamadan affect the incidence of gastric cancer. It is recommended that policymakers and managers inform the public about the risk factors and prevention of gastric cancer through the publication of brochures, television and newspapers.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41542930","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}
Haniyeh Aliakbar, M. Parsaeian, E. Ahmadnezhad, M. Tajvar, M. Yaseri
Introduction: Universal health coverage is a critical goal for low- and middle-income countries, with equitable access to healthcare services being essential to achieving this objective. With the elderly population requiring greater healthcare services, it is crucial to plan for their healthcare needs. This study aims to evaluate the determinants of out-of-pocket payment (OOP) and catastrophic healthcare expenditure among households with elderly individuals in Iran. Methods: This study analyzed the 2018 Household Income-Expenditure Survey in Iran to examine the socio-economic factors affecting OOP (per purchasing power parity International Doller – PPP. Int $) and catastrophic healthcare expenditure in households with elderly members. Using survey probit regression model with Heckman selection, the study identified determinants of OOP and catastrophic healthcare expenditures. A survey probit regression model with Heckman selection has been applied to identify the determinants of out-of-pocket (OOP) and catastrophic healthcare expenditures. The approach allowed for the examination of variables that may have impacted the likelihood of incurring OOP and catastrophic healthcare expenditures, while accounting for potential selection bias. Results: Rural households (with difference 60.78 PPP. Int$) and non-owning homes (with difference 98.83 PPP.Int$) had higher OOP than their urban and owning counterparts, respectively. Larger households also had higher OOP, with those with five or more members having the highest. High-income households also had higher OOP. Additionally, smaller households had a lower chance of facing catastrophic healthcare expenses. Lastly, the Mills ratio was negative. Conclusion: Our study reveals insufficient observed out-of-pocket (OOP) payments for healthcare in Iran to cover the "needed" OOP, indicating a possible financial burden on households. This highlights the need to address inequalities in healthcare access and expenditure for households with elderly individuals, particularly in rural areas and larger households. Policymakers should implement targeted interventions to reduce OOP for these vulnerable groups. Future research should include socio-economic factors that affect access to healthcare services.
{"title":"Determinants of Out-of-Pocket Health Expenditure and Catastrophic Health Spending among Households with Elderly Individuals in Iran: An Application of the Heckman Model to Control Sample Selection","authors":"Haniyeh Aliakbar, M. Parsaeian, E. Ahmadnezhad, M. Tajvar, M. Yaseri","doi":"10.18502/jbe.v8i4.13350","DOIUrl":"https://doi.org/10.18502/jbe.v8i4.13350","url":null,"abstract":"Introduction: Universal health coverage is a critical goal for low- and middle-income countries, with equitable access to healthcare services being essential to achieving this objective. With the elderly population requiring greater healthcare services, it is crucial to plan for their healthcare needs. This study aims to evaluate the determinants of out-of-pocket payment (OOP) and catastrophic healthcare expenditure among households with elderly individuals in Iran. \u0000Methods: This study analyzed the 2018 Household Income-Expenditure Survey in Iran to examine the socio-economic factors affecting OOP (per purchasing power parity International Doller – PPP. Int $) and catastrophic healthcare expenditure in households with elderly members. Using survey probit regression model with Heckman selection, the study identified determinants of OOP and catastrophic healthcare expenditures. A survey probit regression model with Heckman selection has been applied to identify the determinants of out-of-pocket (OOP) and catastrophic healthcare expenditures. The approach allowed for the examination of variables that may have impacted the likelihood of incurring OOP and catastrophic healthcare expenditures, while accounting for potential selection bias. \u0000Results: Rural households (with difference 60.78 PPP. Int$) and non-owning homes (with difference 98.83 PPP.Int$) had higher OOP than their urban and owning counterparts, respectively. Larger households also had higher OOP, with those with five or more members having the highest. High-income households also had higher OOP. Additionally, smaller households had a lower chance of facing catastrophic healthcare expenses. Lastly, the Mills ratio was negative. \u0000Conclusion: Our study reveals insufficient observed out-of-pocket (OOP) payments for healthcare in Iran to cover the \"needed\" OOP, indicating a possible financial burden on households. This highlights the need to address inequalities in healthcare access and expenditure for households with elderly individuals, particularly in rural areas and larger households. Policymakers should implement targeted interventions to reduce OOP for these vulnerable groups. Future research should include socio-economic factors that affect access to healthcare services.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42848214","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}
B. Jamshidi, H. Bekrizadeh, Shahriar Jamshidi Zargaran, M. Rezaei
Introduction: There are different mathematical models describing the propagation of an epidemic. These models can be divided into phenomenological, compartmental, deep learning, and individual-based methods. From other viewpoints, we can classify them into macroscopic or microscopic, stochastic or deterministic, homogeneous or heterogeneous, univariate or multivariate, parsimonious or complex, or forecasting or mechanistic. This paper defines a novel univariate bi-partite time series model able to describe spreading a communicable infection in a population in terms of the relative increment of the cumulative number of confirmed cases. The introduced model can describe different stages of the first wave of the outbreak of a communicable disease from the start to the end. Results: We use it to describe the propagation of various disease outbreaks, including the SARS (2003), the MERS (2018), the Ebola (2014-2016), the HIV/AIDS (1990-2018), the Cholera (2008-2009), and the COVID-19 epidemic in Iran, Italy, the UK, the USA, China and four of its provinces; Beijing, Guangdong, Shanghai, and Hubei (2020). In all mentioned cases, the model has an acceptable performance. In addition, we compare the goodness of this model with the ARIMA models by fitting the propagation of COVID-19 in Iran, Italy, the UK, and the USA. Conclusion: The introduced model is flexible enough to describe a broad range of epidemics. In comparison with ARIMA time series models, our model is more initiative and less complicated, it has fewer parameters, the estimation of its parameters is more straightforward, and its forecasts are narrower and more accurate. Due to its simplicity and accuracy, this model is a good tool for epidemiologists and biostatisticians to model the first wave of an epidemic.
{"title":"A New Family of Time Series to Model the Decreasing Relative Increment of Spreading of an Outbreak","authors":"B. Jamshidi, H. Bekrizadeh, Shahriar Jamshidi Zargaran, M. Rezaei","doi":"10.18502/jbe.v8i4.13352","DOIUrl":"https://doi.org/10.18502/jbe.v8i4.13352","url":null,"abstract":"Introduction: There are different mathematical models describing the propagation of an epidemic. These models can be divided into phenomenological, compartmental, deep learning, and individual-based methods. From other viewpoints, we can classify them into macroscopic or microscopic, stochastic or deterministic, homogeneous or heterogeneous, univariate or multivariate, parsimonious or complex, or forecasting or mechanistic. This paper defines a novel univariate bi-partite time series model able to describe spreading a communicable infection in a population in terms of the relative increment of the cumulative number of confirmed cases. The introduced model can describe different stages of the first wave of the outbreak of a communicable disease from the start to the end. \u0000Results: We use it to describe the propagation of various disease outbreaks, including the SARS (2003), the MERS (2018), the Ebola (2014-2016), the HIV/AIDS (1990-2018), the Cholera (2008-2009), and the COVID-19 epidemic in Iran, Italy, the UK, the USA, China and four of its provinces; Beijing, Guangdong, Shanghai, and Hubei (2020). In all mentioned cases, the model has an acceptable performance. In addition, we compare the goodness of this model with the ARIMA models by fitting the propagation of COVID-19 in Iran, Italy, the UK, and the USA. \u0000Conclusion: The introduced model is flexible enough to describe a broad range of epidemics. In comparison with ARIMA time series models, our model is more initiative and less complicated, it has fewer parameters, the estimation of its parameters is more straightforward, and its forecasts are narrower and more accurate. Due to its simplicity and accuracy, this model is a good tool for epidemiologists and biostatisticians to model the first wave of an epidemic.","PeriodicalId":34310,"journal":{"name":"Journal of Biostatistics and Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47930317","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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