Regression Models as a Tool in Medical Research
portes grátis
Regression Models as a Tool in Medical Research
Vach, Werner
Taylor & Francis Ltd
01/2023
496
Mole
Inglês
9781032477510
15 a 20 dias
920
Descrição não disponível.
THE BASICS: Why Use Regression Models? An Introductory Example. The Classical Multiple Regression Model. Adjusted Effects. Inference for the Classical Multiple Regression Model. Logistic Regression. Inference for the Logistic Regression Model. Categorical Covariates. Handling Ordered Categories: A First Lesson in Regression Modeling Strategies. The Cox Proportional Hazard Model. Common Pitfalls in Using Regression Models. ADVANCED TOPICS AND TECHNIQUES: Some Useful Technicalities. Comparing Regression Coefficients. Power and Sample Size. The Selection of the Sample. The Selection of Covariates. Modeling Nonlinear Effects. Transformation of Covariates. Effect Modification and Interactions. Applying Regression Models to Clustered Data. Applying Regression Models to Longitudinal Data. The Impact of Measurement Error. The Impact of Incomplete Covariate Data. RISK SCORES AND PREDICTORS: Risk Scores. Construction of Predictors. Evaluating the Predictive Performance. Outlook: Construction of Parsimonious Predictors. MISCELLANEOUS: Alternatives to Regression Modeling. Specific Regression Models. Specific Usages of Regression Models. What Is a Good Model? Final Remarks on the Role of Prespecified Models and Model Development. MATHEMATICAL DETAILS: Mathematics behind the Classical Linear Regression Model. Mathematics behind the Logistic Regression Model. The Modern Way of Inference. Mathematics for Risk Scores and Predictors. Bibliography. Index.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Root MSE;regression models in medical research;Roc Curve;classical regression model for continuous outcomes;Likelihood Ratio Test LR Chi2;logistic regression model for binary outcomes;Vice Versa;Data Set;Cox proportional hazards model for survival data;Regression Model;modeling of nonlinear and nonadditive effects;LR Chi2;comparison of regression coefficients;Logistic Regression Number;selection of covariates;Calcium Dose;analysis of clustered and longitudinal data;Cox Model;alternatives to regression modeling;estimation and inference techniques;Classical Regression Model;Scatter Plot;Logit Scale;Risk Score;Gee Approach;Robust Standard Errors;Baseline Survival Function;Linear Regression Number;Log Pseudolikelihood;Standard Deviation ?e;Adjusted Effect Estimates;MAR Assumption;Missing Values;Complete Case Analysis;Fractional Polynomials
THE BASICS: Why Use Regression Models? An Introductory Example. The Classical Multiple Regression Model. Adjusted Effects. Inference for the Classical Multiple Regression Model. Logistic Regression. Inference for the Logistic Regression Model. Categorical Covariates. Handling Ordered Categories: A First Lesson in Regression Modeling Strategies. The Cox Proportional Hazard Model. Common Pitfalls in Using Regression Models. ADVANCED TOPICS AND TECHNIQUES: Some Useful Technicalities. Comparing Regression Coefficients. Power and Sample Size. The Selection of the Sample. The Selection of Covariates. Modeling Nonlinear Effects. Transformation of Covariates. Effect Modification and Interactions. Applying Regression Models to Clustered Data. Applying Regression Models to Longitudinal Data. The Impact of Measurement Error. The Impact of Incomplete Covariate Data. RISK SCORES AND PREDICTORS: Risk Scores. Construction of Predictors. Evaluating the Predictive Performance. Outlook: Construction of Parsimonious Predictors. MISCELLANEOUS: Alternatives to Regression Modeling. Specific Regression Models. Specific Usages of Regression Models. What Is a Good Model? Final Remarks on the Role of Prespecified Models and Model Development. MATHEMATICAL DETAILS: Mathematics behind the Classical Linear Regression Model. Mathematics behind the Logistic Regression Model. The Modern Way of Inference. Mathematics for Risk Scores and Predictors. Bibliography. Index.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Root MSE;regression models in medical research;Roc Curve;classical regression model for continuous outcomes;Likelihood Ratio Test LR Chi2;logistic regression model for binary outcomes;Vice Versa;Data Set;Cox proportional hazards model for survival data;Regression Model;modeling of nonlinear and nonadditive effects;LR Chi2;comparison of regression coefficients;Logistic Regression Number;selection of covariates;Calcium Dose;analysis of clustered and longitudinal data;Cox Model;alternatives to regression modeling;estimation and inference techniques;Classical Regression Model;Scatter Plot;Logit Scale;Risk Score;Gee Approach;Robust Standard Errors;Baseline Survival Function;Linear Regression Number;Log Pseudolikelihood;Standard Deviation ?e;Adjusted Effect Estimates;MAR Assumption;Missing Values;Complete Case Analysis;Fractional Polynomials
