The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Hak Jong Lee - One of the best experts on this subject based on the ideXlab platform.
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image based clinical decision support for transrectal ultrasound in the diagnosis of prostate cancer comparison of Multiple Logistic Regression artificial neural network and support vector machine
European Radiology, 2010Co-Authors: Hak Jong Lee, Sung Il Hwang, Seokmin Han, Seong Ho Park, Seung Hyup Kim, Jeong Yeon Cho, Chang Gyu Seong, Gheeyoung ChoeAbstract:We developed a Multiple Logistic Regression model, an artificial neural network (ANN), and a support vector machine (SVM) model to predict the outcome of a prostate biopsy, and compared the accuracies of each model. One thousand and seventy-seven consecutive patients who had undergone transrectal ultrasound (TRUS)-guided prostate biopsy were enrolled in the study. Clinical decision models were constructed from the input data of age, digital rectal examination findings, prostate-specific antigen (PSA), PSA density (PSAD), PSAD in transitional zone, and TRUS findings. The patients were divided into the training and test groups in a randomized fashion. Areas under the receiver operating characteristic (ROC) curve (AUC, Az) were calculated to summarize the overall performance of each decision model for the task of prostate cancer prediction. The Az values of the ROC curves for the use of Multiple Logistic Regression analysis, ANN, and the SVM were 0.768, 0.778, and 0.847, respectively. Pairwise comparison of the ROC curves determined that the performance of the SVM was superior to that of the ANN or the Multiple Logistic Regression model. Image-based clinical decision support models allow patients to be informed of the actual probability of having a prostate cancer.
Peter Young - One of the best experts on this subject based on the ideXlab platform.
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svy_Logistic_Regression a generic sas macro for simple and Multiple Logistic Regression and creating quality publication ready tables using survey or non survey data
PLOS ONE, 2019Co-Authors: Jacques Muthusi, Samuel Mwalili, Peter YoungAbstract:Introduction Reproducible research is increasingly gaining interest in the research community. Automating the production of research manuscript tables from statistical software can help increase the reproducibility of findings. Logistic Regression is used in studying disease prevalence and associated factors in epidemiological studies and can be easily performed using widely available software including SAS, SUDAAN, Stata or R. However, output from these software must be processed further to make it readily presentable. There exists a number of procedures developed to organize Regression output, though many of them suffer limitations of flexibility, complexity, lack of validation checks for input parameters, as well as inability to incorporate survey design. Methods We developed a SAS macro, %svy_Logistic_Regression, for fitting simple and Multiple Logistic Regression models. The macro also creates quality publication-ready tables using survey or non-survey data which aims to increase transparency of data analyses. It further significantly reduces turn-around time for conducting analysis and preparing output tables while also addressing the limitations of existing procedures. In addition, the macro allows for user-specific actions to handle missing data as well as use of replication-based variance estimation methods. Results We demonstrate the use of the macro in the analysis of the 2013–2014 National Health and Nutrition Examination Survey (NHANES), a complex survey designed to assess the health and nutritional status of adults and children in the United States. The output presented here is directly from the macro and is consistent with how Regression results are often presented in the epidemiological and biomedical literature, with unadjusted and adjusted model results presented side by side. Conclusions The SAS code presented in this macro is comprehensive, easy to follow, manipulate and to extend to other areas of interest. It can also be incorporated quickly by the statistician for immediate use. It is an especially valuable tool for generating quality, easy to review tables which can be incorporated directly in a publication.
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svy_Logistic_Regression a generic sas macro for simple and Multiple Logistic Regression and creating quality publication ready tables using survey or non survey data
bioRxiv, 2019Co-Authors: Jacques Muthusi, Samuel Mwalili, Peter YoungAbstract:Abstract Introduction Reproducible research is increasingly gaining interest in the research community. Automating the production of research manuscript tables from statistical software can help increase the reproducibility of findings. Logistic Regression is used in studying disease prevalence and associated factors in epidemiological studies and can be easily performed using widely available software including SAS, SUDAAN, Stata or R. However, output from these software must be processed further to make it readily presentable. There exists a number of procedures developed to organize Regression output, though many of them suffer limitations of flexibility, complexity, lack of validation checks for input parameters, as well as inability to incorporate survey design. Methods We developed a SAS macro, %svy_Logistic_Regression, for fitting simple and Multiple Logistic Regression models. The macro also creates quality publication-ready tables using survey or non-survey data which aims to increase transparency of data analyses. It further significantly reduces turn-around time for conducting analysis and preparing output tables while also addressing the limitations of existing procedures. Results We demonstrate the use of the macro in the analysis of the 2013-2014 National Health and Nutrition Examination Survey (NHANES), a complex survey designed to assess the health and nutritional status of adults and children in the United States. The output presented here is directly from the macro and is consistent with how Regression results are often presented in the epidemiological and biomedical literature, with unadjusted and adjusted model results presented side by side. Conclusions The SAS code presented in this macro is comprehensive, easy to follow, manipulate and to extend to other areas of interest. It can also be incorporated quickly by the statistician for immediate use. It is an especially valuable tool for generating quality, easy to review tables which can be incorporated directly in a publication.
Roel Schats - One of the best experts on this subject based on the ideXlab platform.
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the probability of pregnancy after embryo transfer is affected by the age of the patient cause of infertility number of embryos transferred and the average morphology score as revealed by Multiple Logistic Regression analysis
Human Reproduction, 1995Co-Authors: Tessa J Roseboom, J P W Vermeide, Erik Schoute, J W Lens, Roel SchatsAbstract:Because the process of conception is affected by many variables, a Multiple Logistic Regression analysis was performed to assess (i) the impact and relative weight of both patient and embryo variables and (ii) their possible effects on the probability of a vital pregnancy after embryo transfer. A statistical model was constructed predicting the probability of pregnancy after embryo transfer. The variables that contributed significantly to the predictive value of the model were the age of the patient, the cause of infertility, the number of embryos transferred and the average morphology score of the transferred embryos. Embryo variables appeared to have a significant but modest value in predicting the probability of pregnancy after embryo transfer. Other variables, such as the thickness of the endometrium, were found to have no prognostic value. Moreover, we found that their effect could be explained by the variables already included in the model.
Kwang Hyun Kim - One of the best experts on this subject based on the ideXlab platform.
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tumor size and age predict the risk of malignancy in hurthle cell neoplasm of the thyroid and can therefore guide the extent of initial thyroid surgery
Thyroid, 2010Co-Authors: Tae Hyuk Kim, Jung Ah Lim, Hwa Young Ahn, Eun Kyung Lee, Hye Sook Min, Kyung Won Kim, Yun Hee Choi, Young Joo Park, Do Joon Park, Kwang Hyun KimAbstract:Background: The majority of patients having a diagnosis of Hurthle cell neoplasm (HCN) on fine-needle aspiration (FNA) of a thyroid nodule have a diagnostic thyroid lobectomy to make the final diagnosis. If the nodule is malignant, they require a completion thyroidectomy. The objective of this study was to devise a simple clinical scheme capable of predicting malignancy in patients with cytologic diagnosis of HCN and, therefore, guide the extent of initial thyroid surgery. Methods: A total of 57 patients who underwent thyroid surgery after an FNA diagnosis of HCN were retrospectively studied. The patients were examined for clinical features, preoperative imaging studies, and pathology reports. The risk of malignancy was calculated using a Multiple Logistic Regression model. Results: The overall rate of malignancy in patients with HCN was 46% (26/57). The predictors of malignancy based on Multiple Logistic Regression analysis were tumor size >1.5 cm (odds ratio [95% confidence interval] = 8.0 [1.9–33.4]) a...
Edgar Sowton - One of the best experts on this subject based on the ideXlab platform.
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determinants of success of coronary angioplasty in patients with a chronic total occlusion a Multiple Logistic Regression model to improve selection of patients
Heart, 1993Co-Authors: Kim Tan, Neil Sulke, N Taub, E Watts, S Karani, Edgar SowtonAbstract:OBJECTIVE--To study the determinants of success of coronary angioplasty in patients with chronic total occlusions, and to formulate a Multiple Logistic Regression model to improve selection of patients. DESIGN--A retrospective analysis of clinical and angiographic data on a consecutive series of patients. PATIENTS--312 patients (mean age 55, range 31 to 79 years, 86% men) who underwent coronary angioplasty procedure for a chronic total occlusion between 1981 and 1992. RESULTS--Procedural success was achieved in 191 lesions (61.2%). A major complication occurred in six patients (1.9%). Multiple stepwise Logistic Regression analysis identified the presence of bridging collaterals (p or = 70%) was 91% (95% confidence intervals (95% CI) 83% to 96%) and predictive value for procedural failure (probability < 30%) was 81% (95% CI 64% to 92%). CONCLUSIONS--Percutaneous transluminal coronary angioplasty of chronic total occlusions is associated with a low risk of acute complication. Procedural success is influenced by easily identifiable clinical and angiographic features and the Multiple Regression model described may help to improve selection of patients.
