Pub Date : 2018-01-01DOI: 10.4172/2155-6180.1000389
Mojtaba Meshkat, A. Baghestani, F. Zayeri
The cure rate survival models are generally used to model lifetime data with long term survivors. We assumes the number of competing causes of the event of interest has the Poisson distribution and the time to the event of interest follows the Generalized Birnbaum-Saunders distribution. The Poisson GB-S distribution has been defined and useful representations for its density function have been presented which facilitates obtaining some mathematical properties. The parameters of the model with cure rate have been estimated using the maximum likelihood method. For different sample sizes and censoring percentages, several simulations have been performed and a real data set from the medical area has been analyzed.
{"title":"The Poisson Generalized Birnbaum-Saunders Cure Model and Application in Breast Cancer Data","authors":"Mojtaba Meshkat, A. Baghestani, F. Zayeri","doi":"10.4172/2155-6180.1000389","DOIUrl":"https://doi.org/10.4172/2155-6180.1000389","url":null,"abstract":"The cure rate survival models are generally used to model lifetime data with long term survivors. We assumes the number of competing causes of the event of interest has the Poisson distribution and the time to the event of interest follows the Generalized Birnbaum-Saunders distribution. The Poisson GB-S distribution has been defined and useful representations for its density function have been presented which facilitates obtaining some mathematical properties. The parameters of the model with cure rate have been estimated using the maximum likelihood method. For different sample sizes and censoring percentages, several simulations have been performed and a real data set from the medical area has been analyzed.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"9 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70292442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.4172/2155-6180.1000409
Minoo Qafary, M. Gharanfoli, Seyed Mehdi Qafari
Medium culture optimization is an Effective, available and financially affordable way to improve production of recombinant proteins produced by genetic engineering. The existence of varieties of parameters and different levels for each, makes it complex, time-consuming and expensive to determine the optimum point of all parameters by applying the factorial method. To overcome these difficulties, in this study, Taguchi robust design method was employed. The environmental parameter such as temperature, pH and glutamine concentration in 4 different levels were considered. According to the design of the experiments, FSH titer was measured. In comparison with the control condition, 14.92 fold overexpression was observed. The best level for these parameters was pH=7.0, 28°C and 2 mM Glutamine concentration. Citation: Qafary M, Gharanfoli M, Qafari SM (2018) Taking Advantage of Taguchi Design Method to Optimize Medium Culture Conditions for Producing Recombinant Follicle Stimulating Hormone. J Biom Biostat 9: 409. doi: 10.4172/2155-6180.1000409
培养基优化是一种有效的、可用的、经济上负担得起的方法，可以提高基因工程生产的重组蛋白的产量。由于参数的多样性和每个参数的不同层次的存在，使得用阶乘方法确定所有参数的最优点变得复杂、耗时和昂贵。为了克服这些困难，本研究采用了田口稳健设计方法。考虑了温度、pH、谷氨酰胺浓度等4种不同水平的环境参数。根据实验设计，测定卵泡刺激素滴度。与对照组相比，过表达量为14.92倍。这些参数的最佳水平为pH=7.0, 28°C, 2 mM谷氨酰胺浓度。引用本文:Qafary M, Gharanfoli M, Qafari SM(2018)利用田口设计法优化生产重组促卵泡激素的培养基条件。[J]中国生物医学工程学报，9(4):444 - 444。doi: 10.4172 / 2155 - 6180.1000409
{"title":"Taking Advantage of Taguchi Design Method to Optimize Medium Culture Conditions for Producing Recombinant Follicle Stimulating Hormone","authors":"Minoo Qafary, M. Gharanfoli, Seyed Mehdi Qafari","doi":"10.4172/2155-6180.1000409","DOIUrl":"https://doi.org/10.4172/2155-6180.1000409","url":null,"abstract":"Medium culture optimization is an Effective, available and financially affordable way to improve production of recombinant proteins produced by genetic engineering. The existence of varieties of parameters and different levels for each, makes it complex, time-consuming and expensive to determine the optimum point of all parameters by applying the factorial method. To overcome these difficulties, in this study, Taguchi robust design method was employed. The environmental parameter such as temperature, pH and glutamine concentration in 4 different levels were considered. According to the design of the experiments, FSH titer was measured. In comparison with the control condition, 14.92 fold overexpression was observed. The best level for these parameters was pH=7.0, 28°C and 2 mM Glutamine concentration. Citation: Qafary M, Gharanfoli M, Qafari SM (2018) Taking Advantage of Taguchi Design Method to Optimize Medium Culture Conditions for Producing Recombinant Follicle Stimulating Hormone. J Biom Biostat 9: 409. doi: 10.4172/2155-6180.1000409","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"09 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000409","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70292679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.4172/2155-6180.1000405
Tsitsiashvili Gurami
Previously an algorithm is constructed to replace each cluster (a class of cyclically equivalent vertices) in a directed graph (representing a protein network) with an acyclic sub-graph preserving all input and output vertices. This algorithm is continuing by an introduction of minimal number of edges between the output vertices and the input vertices in order to introduce feedbacks, stabilizing the functioning of the network. Citation: Gurami T (2018) Construction of Sub-Clusters in the Cluster of Graph Representing the Protein Network. J Biom Biostat 9: 405. doi: 10.4172/2155-6180.1000405
先前构造了一种算法，将有向图(表示蛋白质网络)中的每个簇(一类循环等效顶点)替换为保留所有输入和输出顶点的无环子图。该算法通过引入输出点和输入点之间的最小边数来继续引入反馈，稳定网络的功能。引用本文:Gurami T (2018) the Cluster of Graph of Cluster of Sub-Clusters of representation the Protein Network。[J] .中国生物医学工程学报，9(5):444。doi: 10.4172 / 2155 - 6180.1000405
{"title":"Construction of Sub-Clusters in the Cluster of Graph Representing the Protein Network","authors":"Tsitsiashvili Gurami","doi":"10.4172/2155-6180.1000405","DOIUrl":"https://doi.org/10.4172/2155-6180.1000405","url":null,"abstract":"Previously an algorithm is constructed to replace each cluster (a class of cyclically equivalent vertices) in a directed graph (representing a protein network) with an acyclic sub-graph preserving all input and output vertices. This algorithm is continuing by an introduction of minimal number of edges between the output vertices and the input vertices in order to introduce feedbacks, stabilizing the functioning of the network. Citation: Gurami T (2018) Construction of Sub-Clusters in the Cluster of Graph Representing the Protein Network. J Biom Biostat 9: 405. doi: 10.4172/2155-6180.1000405","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"09 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000405","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70292879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01Epub Date: 2018-02-28DOI: 10.4172/2155-6180.1000392
Fang Xia, Jing Ning, Xuelin Huang
When analyzing time-to-event data in a non-parametric setting without considering covariates, the Kaplan-Meier estimator is widely used to estimate the survival function. When considering covariates, the Cox proportional hazards model is widely used to account for covariates effects. In this setting, for the baseline survival function, the most commonly used approach is the Breslow method, which estimates the baseline survival function as an exponential function of the cumulative baseline hazard function. However, an unnatural and undesirable feature of the Breslow estimator is that, its estimated survival probability will never reaches zero even if the last observation is an event. In this article, we consider an less commonly used alternative, the Kalbfleisch Prentice estimator for the baseline survival function. It is the counterpart of the Kaplan-Meier estimator in a setting with covariates, and thus similarly as the Kaplan Meier estimator, it will reach zero if the last observation is an event. To evaluate the usefulness of the Kalbfleisch Prentice estimator and its relative performance comparing with the Breslow estimator, we conduct simulation studies across a range of conditions by varying the true survival time distribution, sample size, censoring rate and covariate values. We compare the performance of the two estimators regarding bias, mean squared error and relative mean squared error. In most situations in our study, the Kalbfleisch Prentice estimator results in less bias and smaller mean squared error than the Breslow estimator. Their differences are especially clear at the tail of the distribution. The implications of such differences in applications are discussed. We advocate the use of Kalbfleisch Prentice estimator in practice, and further research on its properties.
{"title":"Empirical Comparison of the Breslow Estimator and the Kalbfleisch Prentice Estimator for Survival Functions.","authors":"Fang Xia, Jing Ning, Xuelin Huang","doi":"10.4172/2155-6180.1000392","DOIUrl":"https://doi.org/10.4172/2155-6180.1000392","url":null,"abstract":"<p><p>When analyzing time-to-event data in a non-parametric setting without considering covariates, the Kaplan-Meier estimator is widely used to estimate the survival function. When considering covariates, the Cox proportional hazards model is widely used to account for covariates effects. In this setting, for the baseline survival function, the most commonly used approach is the Breslow method, which estimates the baseline survival function as an exponential function of the cumulative baseline hazard function. However, an unnatural and undesirable feature of the Breslow estimator is that, its estimated survival probability will never reaches zero even if the last observation is an event. In this article, we consider an less commonly used alternative, the Kalbfleisch Prentice estimator for the baseline survival function. It is the counterpart of the Kaplan-Meier estimator in a setting with covariates, and thus similarly as the Kaplan Meier estimator, it will reach zero if the last observation is an event. To evaluate the usefulness of the Kalbfleisch Prentice estimator and its relative performance comparing with the Breslow estimator, we conduct simulation studies across a range of conditions by varying the true survival time distribution, sample size, censoring rate and covariate values. We compare the performance of the two estimators regarding bias, mean squared error and relative mean squared error. In most situations in our study, the Kalbfleisch Prentice estimator results in less bias and smaller mean squared error than the Breslow estimator. Their differences are especially clear at the tail of the distribution. The implications of such differences in applications are discussed. We advocate the use of Kalbfleisch Prentice estimator in practice, and further research on its properties.</p>","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"9 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000392","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36614317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.4172/2155-6180.1000393
A. Yazdani, A. Yazdani
Background: Data preparation, such as missing values imputation and transformation, is the first step in any data analysis and requires crucial attention. We take advantage of availability of replication samples to identify the empirical distribution of missing values through utilization of statistical techniques. We apply these techniques to metabolomics data for imputation. Results: Using replication samples, we obtained the empirical distribution of missing values. After application of the techniques on metabolites, we observed that the rate of missing values is approximately distributed uniformly across metabolite range. Therefore, the missing values cannot be imputed with the lowest values. To have a realistic simulation, we designed a simulation study based on empirical distribution of missing values to find an optimal imputation approach. Our findings validated the optimal approach introduced previously for metabolomics. Conclusions: Our analysis utilized replication samples as a new approach to metabolite imputation and found empirical distribution of missing values, designed a simulation study close to reality, and compared different approaches for selecting an optimal imputation approach. The result of this study validated the optimal approach for metabolite imputation through a different data set and different approach, and the aim was to encourage researchers to pay more attention to metabolite imputation since imputing metabolomic missing values with lowest value is going to be a common approach, for example in genomic-metabolomic data analysis.
{"title":"Using Statistical Techniques and Replication Samples for Missing Values Imputation with an Application on Metabolomics","authors":"A. Yazdani, A. Yazdani","doi":"10.4172/2155-6180.1000393","DOIUrl":"https://doi.org/10.4172/2155-6180.1000393","url":null,"abstract":"Background: Data preparation, such as missing values imputation and transformation, is the first step in any data analysis and requires crucial attention. We take advantage of availability of replication samples to identify the empirical distribution of missing values through utilization of statistical techniques. We apply these techniques to metabolomics data for imputation. Results: Using replication samples, we obtained the empirical distribution of missing values. After application of the techniques on metabolites, we observed that the rate of missing values is approximately distributed uniformly across metabolite range. Therefore, the missing values cannot be imputed with the lowest values. To have a realistic simulation, we designed a simulation study based on empirical distribution of missing values to find an optimal imputation approach. Our findings validated the optimal approach introduced previously for metabolomics. Conclusions: Our analysis utilized replication samples as a new approach to metabolite imputation and found empirical distribution of missing values, designed a simulation study close to reality, and compared different approaches for selecting an optimal imputation approach. The result of this study validated the optimal approach for metabolite imputation through a different data set and different approach, and the aim was to encourage researchers to pay more attention to metabolite imputation since imputing metabolomic missing values with lowest value is going to be a common approach, for example in genomic-metabolomic data analysis.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"9 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000393","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70292533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.4172/2155-6180.1000401
Jingke Zhou, Yingzhen Chen
Density Weighted Variance (DWV), a novel model-free feature screening criterion is proposed for mean regression with ultrahigh-dimensional covariates. Compared with existing model free screening criteria, DWV criterion possesses faster convergence rate for inactive co-varieties and is as same convergence rate as most existing variable screening procedures for active covariates. Furthermore, DWV criterion is extended to quintile regression and multiple response regression setting. Finally, numerical simulations and a real data analysis are conducted to show the finite sample performance of the proposed methods.
{"title":"Ultra-high Dimensional Variable Screening via Density Weighted Variance","authors":"Jingke Zhou, Yingzhen Chen","doi":"10.4172/2155-6180.1000401","DOIUrl":"https://doi.org/10.4172/2155-6180.1000401","url":null,"abstract":"Density Weighted Variance (DWV), a novel model-free feature screening criterion is proposed for mean regression with ultrahigh-dimensional covariates. Compared with existing model free screening criteria, DWV criterion possesses faster convergence rate for inactive co-varieties and is as same convergence rate as most existing variable screening procedures for active covariates. Furthermore, DWV criterion is extended to quintile regression and multiple response regression setting. Finally, numerical simulations and a real data analysis are conducted to show the finite sample performance of the proposed methods.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"9 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000401","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70292571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.4172/2155-6180.1000406
S. Jiang, R. Cook
Studies of the development and growth of organisms are often conducted in laboratories where organisms maintained in tanks are examined repeatedly over time. Collection and recording of cross-sectional aggregate count data on stage occupancy is both less expensive and administratively more convenient than tracking the stages of each organism over time. In such settings tank to tank variation must also be taken into account as growth rates may be more similar among organisms within the same tank than for those in different tanks. We consider the cost effect design of a prospective developmental study of organisms based on a marginal Markov model which deals with between tank variation and within tank dependence. We develop a flexible design in which some tanks provide repeated cross-sectional aggregate data, and other tanks provide serial responses through tracking individuals. We assess the relative efficiency of aggregate and individual-level longitudinal data. The optimal cost-effective design is shown to depend on whether primary interest lies in transition intensities or associated cluster-level covariate effects. Citation: Jiang S, Cook RJ (2018) Cost-effective Design of Growth Studies with Aggregation and Tracking. J Biom Biostat 9: 406. doi: 10.4172/21556180.1000406
{"title":"Cost-effective Design of Growth Studies with Aggregation and Tracking","authors":"S. Jiang, R. Cook","doi":"10.4172/2155-6180.1000406","DOIUrl":"https://doi.org/10.4172/2155-6180.1000406","url":null,"abstract":"Studies of the development and growth of organisms are often conducted in laboratories where organisms maintained in tanks are examined repeatedly over time. Collection and recording of cross-sectional aggregate count data on stage occupancy is both less expensive and administratively more convenient than tracking the stages of each organism over time. In such settings tank to tank variation must also be taken into account as growth rates may be more similar among organisms within the same tank than for those in different tanks. We consider the cost effect design of a prospective developmental study of organisms based on a marginal Markov model which deals with between tank variation and within tank dependence. We develop a flexible design in which some tanks provide repeated cross-sectional aggregate data, and other tanks provide serial responses through tracking individuals. We assess the relative efficiency of aggregate and individual-level longitudinal data. The optimal cost-effective design is shown to depend on whether primary interest lies in transition intensities or associated cluster-level covariate effects. Citation: Jiang S, Cook RJ (2018) Cost-effective Design of Growth Studies with Aggregation and Tracking. J Biom Biostat 9: 406. doi: 10.4172/21556180.1000406","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"09 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000406","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70292968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.4172/2155-6180.1000411
M. Begum, P. Sultana
Background: Smokeless tobacco is also highly addictive and causes cancer of the head and neck, esophagus and pancreas, besides many oral diseases. Bangladesh is one of the most prevalent smokeless tobacco consumption countries in the world. This paper aimed to examine the socioeconomic and demographic factors patterning smokeless tobacco consumption among adults aged 15 years and above in Bangladesh using multilevel analysis. Materials and methods: A cross sectional, nationally representative sample of individuals from the Global Adult Tobacco Survey in Bangladesh (2010), which covered 9629 individuals aged 15 years and above using multi-stage stratified cluster sampling has been analyzed. Smokeless tobacco use daily was considered as outcome variable. Multilevel logistic regression analysis has been used with individuals nested within clusters. Measures of association (odds ratio) and measures of variance (intra-class correlation (ICC)) have been calculated, as well as the discriminatory accuracy by calculating the area under the ROC curve (AUC). Also the comparison between single and multilevel model has been done to investigate the necessity of multilevel effects. Results: According to the multilevel logistic regression model female use smokeless tobacco more than male (odds ratio (OR): 1.72, 95% CI: 1.39, 2.07). The use of smokeless tobacco by age was highest among older group (>46 years) than youngest group (≤24 years) (OR: 16.04, 95% CI: 12.60, 20.53). The smokeless tobacco use was highest among the least educated (no formal education) (OR=4.93, 95% CI: 3.28, 7.41) compared to highest educated (college/university completed or above) respondent. Respondents from the poorest wealth index were significantly more likely to consume smokeless tobacco (OR 1.67, 95%CI: 1.33, 2.09) compared to respondents of richest wealth index. Conclusions: There is an urgent need to curb the use of smokeless tobacco among female, less educated, older and of lowest wealth index. Tobacco control policies in Bangladesh should adopt a targeted, population-based approach to control and reduce tobacco consumption considering of socioeconomic and demographic factors to make it successful in the country. Citation: Begum M, Sultana P (2018) Socioeconomic and Demographic Factors Patterning Smokeless Tobacco Use Behavior in Bangladesh: A Cross-Sectional Multilevel Analysis. J Biom Biostat 9: 411. doi: 10.4172/2155-6180.1000411
{"title":"Socioeconomic and Demographic Factors Patterning Smokeless Tobacco Use Behavior in Bangladesh: A Cross-Sectional Multilevel Analysis","authors":"M. Begum, P. Sultana","doi":"10.4172/2155-6180.1000411","DOIUrl":"https://doi.org/10.4172/2155-6180.1000411","url":null,"abstract":"Background: Smokeless tobacco is also highly addictive and causes cancer of the head and neck, esophagus and pancreas, besides many oral diseases. Bangladesh is one of the most prevalent smokeless tobacco consumption countries in the world. This paper aimed to examine the socioeconomic and demographic factors patterning smokeless tobacco consumption among adults aged 15 years and above in Bangladesh using multilevel analysis. Materials and methods: A cross sectional, nationally representative sample of individuals from the Global Adult Tobacco Survey in Bangladesh (2010), which covered 9629 individuals aged 15 years and above using multi-stage stratified cluster sampling has been analyzed. Smokeless tobacco use daily was considered as outcome variable. Multilevel logistic regression analysis has been used with individuals nested within clusters. Measures of association (odds ratio) and measures of variance (intra-class correlation (ICC)) have been calculated, as well as the discriminatory accuracy by calculating the area under the ROC curve (AUC). Also the comparison between single and multilevel model has been done to investigate the necessity of multilevel effects. Results: According to the multilevel logistic regression model female use smokeless tobacco more than male (odds ratio (OR): 1.72, 95% CI: 1.39, 2.07). The use of smokeless tobacco by age was highest among older group (>46 years) than youngest group (≤24 years) (OR: 16.04, 95% CI: 12.60, 20.53). The smokeless tobacco use was highest among the least educated (no formal education) (OR=4.93, 95% CI: 3.28, 7.41) compared to highest educated (college/university completed or above) respondent. Respondents from the poorest wealth index were significantly more likely to consume smokeless tobacco (OR 1.67, 95%CI: 1.33, 2.09) compared to respondents of richest wealth index. Conclusions: There is an urgent need to curb the use of smokeless tobacco among female, less educated, older and of lowest wealth index. Tobacco control policies in Bangladesh should adopt a targeted, population-based approach to control and reduce tobacco consumption considering of socioeconomic and demographic factors to make it successful in the country. Citation: Begum M, Sultana P (2018) Socioeconomic and Demographic Factors Patterning Smokeless Tobacco Use Behavior in Bangladesh: A Cross-Sectional Multilevel Analysis. J Biom Biostat 9: 411. doi: 10.4172/2155-6180.1000411","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000411","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70293397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.4172/2155-6180.1000388
Sunday Babuba
In this paper, we consider the system of algebraic equations arising from the discretization of elliptic partial differential equation with respect to x and y axes. To compute the solution of the resulting equations we use the new method to solve various elliptic equations. We study the numerical accuracy of the method. The numerical results have shown that the method provided exact result depending on the particular equation on which the scheme is applied.
{"title":"Interpolation-Collocation Method of Solution for Solving Poisson Equation","authors":"Sunday Babuba","doi":"10.4172/2155-6180.1000388","DOIUrl":"https://doi.org/10.4172/2155-6180.1000388","url":null,"abstract":"In this paper, we consider the system of algebraic equations arising from the discretization of elliptic partial differential equation with respect to x and y axes. To compute the solution of the resulting equations we use the new method to solve various elliptic equations. We study the numerical accuracy of the method. The numerical results have shown that the method provided exact result depending on the particular equation on which the scheme is applied.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"9 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4172/2155-6180.1000388","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70292362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01DOI: 10.4172/2155-6180.1000402
D. T. Ajayi, S. Bello
Poor nutrition during pregnancy is a major public health problem. Maternal under nutrition is a significant risk factor for maternal morbidity, mortality, poor birth outcomes (e.g. low birth weight), and infant mortality. Maternal under nutrition is defined as having a body mass index (BMI) <18.5 kg/m2. Previous studies on maternal BMI utilized classical statistical approach, whose criteria for model assessment are goodness-of-fit test and residual examination. The aim of this study was to identify predictors of BMI among pregnant women in Nigeria, and to compare the performance of ordinary least squares (OLS) regression and quantile regression using machine learning approach. This study utilized data from the 2013 Nigeria Demographic and Health Survey. A total of 3,049 pregnant women were included in the study. Data were summarized using descriptive statistics. The assumption of normality of the outcome variable (BMI) was tested using one-sample Kolmogorov-Smirnov test. Bivariate associations of BMI with independent variables were assessed using robust (nonparametric) statistical techniques: Kendall’s tau correlation for continuous predictors, Wilcoxon rank sum test for binary predictors and Kruskal-Wallis test for multinomial predictors. Predictors of maternal BMI were investigated using OLS and quantile regression analyses. Model assessment was made using 10-fold cross-validation. A two-tailed p-value <0.05 was considered statistically significant. The respondents had a mean age of 28.22 ± 6.30 years, and a mean BMI of 23.81 ± 4.18 kg/m2. Multivariate analyses identified respondent’s age, duration of pregnancy, wealth class, and residence as predictors of maternal BMI. The crossvalidated mean squared error for the OLS regression model was lower than that for the quantile regression model. Respondent’s age, duration of pregnancy, wealth class, and residence were significantly associated with maternal BMI. OLS regression model fit the data more than the quantile regression model. Citation: Ajayi DT, Bello S (2018) Predictors of Body Mass Index among Pregnant Women in Nigeria: A Comparison of Ordinary Least Squares Regression and Quantile Regression Models Using Machine Learning Approach. J Biom Biostat 9: 402. doi: 10.4172/2155-6180.1000402
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