Werner Vach

Regression Models as a Tool in Medical Research

• Gebundenes Buch

Produktbeschreibung

While regression models have become standard tools in medical research, understanding how to properly apply the models and interpret the results is often challenging for beginners. This text presents the fundamental concepts and important aspects of regression models most commonly used in medical research, including the classical regression model for continuous outcomes, the logistic regression model for binary outcomes, and the Cox proportional hazards model for survival data. The author emphasizes adequate use, correct interpretation of results, appropriate presentation of results, and avoidance of potential pitfalls.

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Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 496
• Erscheinungstermin: 27. November 2012
• Englisch
• Abmessung: 238mm x 161mm x 38mm
• Gewicht: 840g
• ISBN-13: 9781466517486
• ISBN-10: 1466517484
• Artikelnr.: 36527571

Autorenporträt

Werner Vach is a professor of medical informatics and clinical epidemiology at the University of Freiburg. Dr. Vach has co-authored more than 150 publications in medical journals. His research encompasses biostatistics methodology in the areas of incomplete covariate data, prognostic studies, diagnostic studies, and agreement studies.

Inhaltsangabe

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.