Regression methods in biostatistics: linear, logistic, survival, and repeated measures models

This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear modelsfor continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes. Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation,and interaction of causal effects in essentially the same way. The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. Short and to the point so that the important issues and similarities between the methods, rather than the differences, shine through. Provides a unifiedintroduction to the regression methods listed in the title. Unified approach should appeal to students who learn conceptually and verbally. INDICE: Introduction. Exploratory and Descriptive Methods. Basic Statistical Methods. Linear Regression. Logistic Regression. Survival Analysis. Repeated Measures Analysis. Generalized Linear Models. Strengthening Casual Inference. Predictor Selection. Complex Surveys. Summary.

• ISBN: 978-1-4614-1352-3
• Editorial: Springer New York
• Encuadernacion: Cartoné
• Páginas: 530
• Fecha Publicación: 29/12/2011
• Nº Volúmenes: 1
• Idioma: Inglés