Logic regression is an adaptive regression method which searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome, and thus, it reveals interaction effects which ar...Logic regression is an adaptive regression method which searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome, and thus, it reveals interaction effects which are associated with the response. In this study, we extended logic regression to longitudinal data with binary response and proposed “Transition Logic Regression Method” to find interactions related to response. In this method, interaction effects over time were found by Annealing Algorithm with AIC (Akaike Information Criterion) as the score function of the model. Also, first and second orders Markov dependence were allowed to capture the correlation among successive observations of the same individual in longitudinal binary response. Performance of the method was evaluated with simulation study in various conditions. Proposed method was used to find interactions of SNPs and other risk factors related to low HDL over time in data of 329 participants of longitudinal TLGS study.展开更多
Accurate prognosis in patients with lung cancer is important for clinical decision making and treatment selection. The TNM staging system is currently the main method for establishing prognosis. Using this system, pat...Accurate prognosis in patients with lung cancer is important for clinical decision making and treatment selection. The TNM staging system is currently the main method for establishing prognosis. Using this system, patients are grouped into one of four stages based on primary tumor extent, nodal disease, and distant metastases. However, each stage represents a range of disease extent and may not on its own be the best reflection of individual patient prognosis. 18F-fluorodeoxyglucose_positron emission tomography （18F-FDG-PET） can be used to evaluate the metabolic tumor burden affecting the whole body with measures such as metabolic rumor volume （MTV） and total lesion glycolysis （TLG）. MTV and TLG have been shown to be significant prognostic factors in patients with lung cancer, independent of TNM stage. These metabolic tumor burden measures have the potential to make lung cancer staging and prognostication more accurate and quantitative, with the goal of optimizing treatment choices and outcome predictions.展开更多
文摘Logic regression is an adaptive regression method which searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome, and thus, it reveals interaction effects which are associated with the response. In this study, we extended logic regression to longitudinal data with binary response and proposed “Transition Logic Regression Method” to find interactions related to response. In this method, interaction effects over time were found by Annealing Algorithm with AIC (Akaike Information Criterion) as the score function of the model. Also, first and second orders Markov dependence were allowed to capture the correlation among successive observations of the same individual in longitudinal binary response. Performance of the method was evaluated with simulation study in various conditions. Proposed method was used to find interactions of SNPs and other risk factors related to low HDL over time in data of 329 participants of longitudinal TLGS study.
文摘Accurate prognosis in patients with lung cancer is important for clinical decision making and treatment selection. The TNM staging system is currently the main method for establishing prognosis. Using this system, patients are grouped into one of four stages based on primary tumor extent, nodal disease, and distant metastases. However, each stage represents a range of disease extent and may not on its own be the best reflection of individual patient prognosis. 18F-fluorodeoxyglucose_positron emission tomography （18F-FDG-PET） can be used to evaluate the metabolic tumor burden affecting the whole body with measures such as metabolic rumor volume （MTV） and total lesion glycolysis （TLG）. MTV and TLG have been shown to be significant prognostic factors in patients with lung cancer, independent of TNM stage. These metabolic tumor burden measures have the potential to make lung cancer staging and prognostication more accurate and quantitative, with the goal of optimizing treatment choices and outcome predictions.
