The Experts below are selected from a list of 1674 Experts worldwide ranked by ideXlab platform
Rochani Haresh - One of the best experts on this subject based on the ideXlab platform.
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Sensitivity Analysis for Non-ignorable Missing Data With Misclassification Error
Digital Commons@Georgia Southern, 2018Co-Authors: Rochani Haresh, Samawi Hani, Yu LiliAbstract:Presentation given by Georgia Southern Faculty members Rochani D. Haresh, Hani M. Samawi, and Yu Lili at the 2018 American Public Health Association (APHA) Meeting. Missing data and misclassification error are very common problem in many research studies. It is well known that the misclassification error in covariates can cause bias estimation of parameters for statistical model as well as it can reduce the overall statistical power. Misclassification simulation extrapolation (MC-SIMEX) procedure is a well-known method to correct the bias in parameter estimation due to misclassification for given statistical model. The misclassification matrix has to be known or estimated from a validation study to use MC-SIMEX method. However, in many circumstances, the validation study has non-ignorable missing data. Estimation of misclassification matrix can be biased due to presence of non-ignorable missing data which lead to bias estimation of parameters for our statistical model. In this paper, we apply the Baker, Rosenberger and Dersimonian modeling approach to perform the sensitivity analysis using MC-SIMEX method. Simulation studies are used to investigate the efficiency of parameters under given assumption of missing data mechanism. We illustrate the method by application to the “National Health and Nutrition Examination Survey” dataset
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Correction of Misclassification Error in Presence of Non-Ignorable Missing Data
Digital Commons@Georgia Southern, 2018Co-Authors: Rochani Haresh, Yu Lili, Samawi HaniAbstract:Presentation given at Eastern North American Region International Biometric Society (ENAR). Abstracts Missing data and misclassification errors are very common problem in many research studies. It is well known that the misclassification error in covariates can cause bias estimation of parameters for statistical model. It can also reduce the overall statistical power. Misclassification simulation extrapolation (MC-SIMEX) procedure is a well-known method to correct the bias in parameter estimation due to misclassification for given statistical model. Misclassification matrix has to be known or estimated from a validation study to use MC-SIMEX method. However, in many circumstances, the validation study has non-ignorable missing data. Estimation of misclassification matrix can be biased and hence the estimation of parameters of given statistical model in presence of non-ignorable missing data. In this paper, we apply the Baker, Rosenberger and Dersimonian modeling approach to perform the sensitivity analysis using MC-SIMEX method. Simulation studies are used to investigate the efficiency of parameters under given assumption of missing data mechanism. We illustrate the method by using “National Health and Nutrition Examination Survey” dataset
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Estimates for Cell Counts and Common Odds Ratio in Three-Way Contingency Tables by Homogeneous Log-Linear Models with Missing Data
'Springer Science and Business Media LLC', 2017Co-Authors: Rochani Haresh, Vogel, Robert L., Samawi, Hani M., Linder DanielAbstract:Missing observations often occur in cross-classified data collected during observational, clinical, and public health studies. Inappropriate treatment of missing data can reduce statistical power and give biased results. This work extends the Baker, Rosenberger and Dersimonian modeling approach to compute maximum likelihood estimates for cell counts in three-way tables with missing data, and studies the association between two dichotomous variables while controlling for a third variable in 2×2×K tables. This approach is applied to the Behavioral Risk Factor Surveillance System data. Simulation studies are used to investigate the efficiency of estimation of the common odds ratio
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Explicit Estimates for Cell Counts and Modeling the Missing Data Indicators in a Three-Way Contingency Table by Log-Linear Models
Digital Commons@Georgia Southern, 2015Co-Authors: Rochani Haresh, Vogel, Robert L., Samawi, Hani M., Linder, Daniel F.Abstract:Missing observations in cross-classified data are an extremely common problem in the process of research in clinical studies, observational studies and public health. Ignorance of missing values in the analysis can produce biased results and low statistical power. The focus of this research is to expand Baker, Rosenberger and Dersimonian (BRD) model approach to compute the explicit maximum likelihood estimates for cell counts for three-way cross-classified data. In case of missing observations, derivation of explicit cell counts for three-way table with supplementary margins can be obtained by controlling the missingness in third variable and by modeling the missing-data indicators using homogeneous log-linear models. Previous methods for contingency tables with supplementary margins required an iterative algorithm, however, expected cell counts for complete cells as well as missing cells can be obtained by simple algebraic formula. We conduct a simulation study with Neuropatholody data to illustrate that the explicit maximum likelihood estimates can produce consistent results
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Maximum Likelihood Estimates by Homogenous Log-Linear Models for Three-Way Contingency Tables with Missing Data with Application to Neuropathology Data
Digital Commons@Georgia Southern, 2015Co-Authors: Rochani Haresh, Vogel, Robert L., Samawi, Hani M., Linder DanielAbstract:Missing observations in cross-classified data are an extremely common problem in the process of research in clinical studies, observational studies and public health. Ignorance of missing values in the analysis can produce biased results and low statistical power. The focus of this research is to expand Baker, Rosenberger and Dersimonian (BRD) model approach to compute the explicit maximum likelihood estimates for cell counts for three-way cross-classified data. In case of missing observations, derivation of explicit cell counts for three-way table with supplementary margins can be obtained by controlling the missingness in third variable and by modeling the missing-data indicators using homogeneous log-linear models. Previous methods for contingency tables with supplementary margins required an iterative algorithm, however, expected cell counts for complete cells as well as missing cells can be obtained by simple algebraic formula. We conduct a simulation study with Neuropathology data to illustrate that the explicit maximum likelihood estimates can produce consistent results
Vogel, Robert L. - One of the best experts on this subject based on the ideXlab platform.
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Estimates for Cell Counts and Common Odds Ratio in Three-Way Contingency Tables by Homogeneous Log-Linear Models with Missing Data
'Springer Science and Business Media LLC', 2017Co-Authors: Rochani Haresh, Vogel, Robert L., Samawi, Hani M., Linder DanielAbstract:Missing observations often occur in cross-classified data collected during observational, clinical, and public health studies. Inappropriate treatment of missing data can reduce statistical power and give biased results. This work extends the Baker, Rosenberger and Dersimonian modeling approach to compute maximum likelihood estimates for cell counts in three-way tables with missing data, and studies the association between two dichotomous variables while controlling for a third variable in 2×2×K tables. This approach is applied to the Behavioral Risk Factor Surveillance System data. Simulation studies are used to investigate the efficiency of estimation of the common odds ratio
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Explicit Estimates for Cell Counts and Modeling the Missing Data Indicators in a Three-Way Contingency Table by Log-Linear Models
Digital Commons@Georgia Southern, 2015Co-Authors: Rochani Haresh, Vogel, Robert L., Samawi, Hani M., Linder, Daniel F.Abstract:Missing observations in cross-classified data are an extremely common problem in the process of research in clinical studies, observational studies and public health. Ignorance of missing values in the analysis can produce biased results and low statistical power. The focus of this research is to expand Baker, Rosenberger and Dersimonian (BRD) model approach to compute the explicit maximum likelihood estimates for cell counts for three-way cross-classified data. In case of missing observations, derivation of explicit cell counts for three-way table with supplementary margins can be obtained by controlling the missingness in third variable and by modeling the missing-data indicators using homogeneous log-linear models. Previous methods for contingency tables with supplementary margins required an iterative algorithm, however, expected cell counts for complete cells as well as missing cells can be obtained by simple algebraic formula. We conduct a simulation study with Neuropatholody data to illustrate that the explicit maximum likelihood estimates can produce consistent results
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Maximum Likelihood Estimates by Homogenous Log-Linear Models for Three-Way Contingency Tables with Missing Data with Application to Neuropathology Data
Digital Commons@Georgia Southern, 2015Co-Authors: Rochani Haresh, Vogel, Robert L., Samawi, Hani M., Linder DanielAbstract:Missing observations in cross-classified data are an extremely common problem in the process of research in clinical studies, observational studies and public health. Ignorance of missing values in the analysis can produce biased results and low statistical power. The focus of this research is to expand Baker, Rosenberger and Dersimonian (BRD) model approach to compute the explicit maximum likelihood estimates for cell counts for three-way cross-classified data. In case of missing observations, derivation of explicit cell counts for three-way table with supplementary margins can be obtained by controlling the missingness in third variable and by modeling the missing-data indicators using homogeneous log-linear models. Previous methods for contingency tables with supplementary margins required an iterative algorithm, however, expected cell counts for complete cells as well as missing cells can be obtained by simple algebraic formula. We conduct a simulation study with Neuropathology data to illustrate that the explicit maximum likelihood estimates can produce consistent results
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Explicit Estimates for Cell Counts and Modeling the Missing Data Indicators in Three-Way Contingency Table by Log-Linear Models
Digital Commons@Georgia Southern, 2015Co-Authors: Rochani Haresh, Vogel, Robert L., Samawi, Hani M., Linder DanielAbstract:Missing observations in cross-classified data are an extremely common problem in the process of research in public health, clinical sciences and social sciences. Ignorance of missing values in the analysis can produce biased results and low statistical power. The purpose of this research was to expand Baker, Rosenberger and Dersimonian (BRD) model approach to compute the explicit maximum likelihood estimates for cell counts for three-way cross-classified data. Derivation of explicit cell counts for three-way table with supplementary margins can be obtained by controlling the missingness in third variable and by modeling the missing-data indicators using homogeneous log-linear models. Previous methods for contingency tables with supplementary margins required an iterative algorithms, however, expected cell counts can be obtained by simple algebraic formula. Simulation study with source of knowledge of cancer data illustrate that how well the explicit maximum likelihood estimates can produce consistent results in idyllic circumstances. Application of the BRD model approach to Slovenian public opinion survey data reveals the effect of smaller sample size to the validity of the method
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Correction of Verification Bias by Application of Homogeneous Log-Linear Models for a Single Binary-Scale Diagnostic Test
Digital Commons@Georgia Southern, 2015Co-Authors: Rochani Haresh, Vogel, Robert L., Samawi, Hani M., Yin JingjingAbstract:In patient management and control of many infectious diseases it is very crucial to have accurate diagnostic test. The test/procedure that determines the true disease status without an error is referred to as gold standard test. Even when a gold standard exist, it is extremely difficult to verify each patient due to the issues of cost-effectiveness and invasive nature of the procedures. The ability of the diagnostic tests to correctly identify the patients with and without the disease can be evaluated by measures such as sensitivity, specificity and predictive values. However, these measures can give biased estimates if we only consider the patients with test results who underwent for gold standard procedure (Verification Bias). The emphasis of this research is to apply Baker, Rosenberger and Dersimonian (BRD) model approach to derive the maximum likelihood estimates and variances for sensitivity, specificity and predictive values by using homogenous log-linear models. We apply this approach to analyze Hepatic Scintigraph data under the assumption of ignorable as well as non-ignorable missing data mechanisms
Samawi, Hani M. - One of the best experts on this subject based on the ideXlab platform.
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Estimates for Cell Counts and Common Odds Ratio in Three-Way Contingency Tables by Homogeneous Log-Linear Models with Missing Data
'Springer Science and Business Media LLC', 2017Co-Authors: Rochani Haresh, Vogel, Robert L., Samawi, Hani M., Linder DanielAbstract:Missing observations often occur in cross-classified data collected during observational, clinical, and public health studies. Inappropriate treatment of missing data can reduce statistical power and give biased results. This work extends the Baker, Rosenberger and Dersimonian modeling approach to compute maximum likelihood estimates for cell counts in three-way tables with missing data, and studies the association between two dichotomous variables while controlling for a third variable in 2×2×K tables. This approach is applied to the Behavioral Risk Factor Surveillance System data. Simulation studies are used to investigate the efficiency of estimation of the common odds ratio
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Explicit Estimates for Cell Counts and Modeling the Missing Data Indicators in a Three-Way Contingency Table by Log-Linear Models
Digital Commons@Georgia Southern, 2015Co-Authors: Rochani Haresh, Vogel, Robert L., Samawi, Hani M., Linder, Daniel F.Abstract:Missing observations in cross-classified data are an extremely common problem in the process of research in clinical studies, observational studies and public health. Ignorance of missing values in the analysis can produce biased results and low statistical power. The focus of this research is to expand Baker, Rosenberger and Dersimonian (BRD) model approach to compute the explicit maximum likelihood estimates for cell counts for three-way cross-classified data. In case of missing observations, derivation of explicit cell counts for three-way table with supplementary margins can be obtained by controlling the missingness in third variable and by modeling the missing-data indicators using homogeneous log-linear models. Previous methods for contingency tables with supplementary margins required an iterative algorithm, however, expected cell counts for complete cells as well as missing cells can be obtained by simple algebraic formula. We conduct a simulation study with Neuropatholody data to illustrate that the explicit maximum likelihood estimates can produce consistent results
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Maximum Likelihood Estimates by Homogenous Log-Linear Models for Three-Way Contingency Tables with Missing Data with Application to Neuropathology Data
Digital Commons@Georgia Southern, 2015Co-Authors: Rochani Haresh, Vogel, Robert L., Samawi, Hani M., Linder DanielAbstract:Missing observations in cross-classified data are an extremely common problem in the process of research in clinical studies, observational studies and public health. Ignorance of missing values in the analysis can produce biased results and low statistical power. The focus of this research is to expand Baker, Rosenberger and Dersimonian (BRD) model approach to compute the explicit maximum likelihood estimates for cell counts for three-way cross-classified data. In case of missing observations, derivation of explicit cell counts for three-way table with supplementary margins can be obtained by controlling the missingness in third variable and by modeling the missing-data indicators using homogeneous log-linear models. Previous methods for contingency tables with supplementary margins required an iterative algorithm, however, expected cell counts for complete cells as well as missing cells can be obtained by simple algebraic formula. We conduct a simulation study with Neuropathology data to illustrate that the explicit maximum likelihood estimates can produce consistent results
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Explicit Estimates for Cell Counts and Modeling the Missing Data Indicators in Three-Way Contingency Table by Log-Linear Models
Digital Commons@Georgia Southern, 2015Co-Authors: Rochani Haresh, Vogel, Robert L., Samawi, Hani M., Linder DanielAbstract:Missing observations in cross-classified data are an extremely common problem in the process of research in public health, clinical sciences and social sciences. Ignorance of missing values in the analysis can produce biased results and low statistical power. The purpose of this research was to expand Baker, Rosenberger and Dersimonian (BRD) model approach to compute the explicit maximum likelihood estimates for cell counts for three-way cross-classified data. Derivation of explicit cell counts for three-way table with supplementary margins can be obtained by controlling the missingness in third variable and by modeling the missing-data indicators using homogeneous log-linear models. Previous methods for contingency tables with supplementary margins required an iterative algorithms, however, expected cell counts can be obtained by simple algebraic formula. Simulation study with source of knowledge of cancer data illustrate that how well the explicit maximum likelihood estimates can produce consistent results in idyllic circumstances. Application of the BRD model approach to Slovenian public opinion survey data reveals the effect of smaller sample size to the validity of the method
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Correction of Verification Bias by Application of Homogeneous Log-Linear Models for a Single Binary-Scale Diagnostic Test
Digital Commons@Georgia Southern, 2015Co-Authors: Rochani Haresh, Vogel, Robert L., Samawi, Hani M., Yin JingjingAbstract:In patient management and control of many infectious diseases it is very crucial to have accurate diagnostic test. The test/procedure that determines the true disease status without an error is referred to as gold standard test. Even when a gold standard exist, it is extremely difficult to verify each patient due to the issues of cost-effectiveness and invasive nature of the procedures. The ability of the diagnostic tests to correctly identify the patients with and without the disease can be evaluated by measures such as sensitivity, specificity and predictive values. However, these measures can give biased estimates if we only consider the patients with test results who underwent for gold standard procedure (Verification Bias). The emphasis of this research is to apply Baker, Rosenberger and Dersimonian (BRD) model approach to derive the maximum likelihood estimates and variances for sensitivity, specificity and predictive values by using homogenous log-linear models. We apply this approach to analyze Hepatic Scintigraph data under the assumption of ignorable as well as non-ignorable missing data mechanisms
Kerstin Rabenstein - One of the best experts on this subject based on the ideXlab platform.
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Rosenberger katharina differenzfahigkeit bei lehramtsstudierenden eine vignettenstudie wiesbaden springer vs 2013 rezension
Erziehungswissenschaftliche Revue, 2014Co-Authors: Kerstin RabensteinAbstract:Rezension von: Katharina Rosenberger: Differenzfahigkeit bei Lehramtsstudierenden. Eine Vignettenstudie. Wiesbaden: Springer VS 2013, 219 S. ISBN 978-3-658-01440-7
Hinzke Jan-hendrik - One of the best experts on this subject based on the ideXlab platform.
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Rosenberger, K. (2013). Differenzfähigkeit bei Lehramtsstudierenden. Eine Vignettenstudie, Wiesbaden: Springer VS, 2013
Schweizerische Gesellschaft für Lehrerinnen- und Lehrerbildung SGL-SSFE, 2015Co-Authors: Hinzke Jan-hendrikAbstract:Hinzke J-H. Rosenberger, K. (2013). Differenzfähigkeit bei Lehramtsstudierenden. Eine Vignettenstudie, Wiesbaden: Springer VS, 2013. Beiträge zur Lehrerinnen- und Lehrerbildung. 2015;33(2):294-295
