Format:
Online-Ressource (XX, 509p. 65 illus, digital)
Edition:
2nd ed. 2012
ISBN:
9781461413530
Series Statement:
Statistics for Biology and Health
Content:
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.
Content:
This new edition provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for 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. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses. In the second edition, the authors have substantially expanded the core chapters, including new coverage of exact, ordinal, and multinomial logistic models, discrete time and competing risks survival models, within and between effects in longitudinal models, zero-inflated Poisson and negative binomial models, cross-validation for prediction model selection, directed acyclic graphs, and sample size, power and minimum detectable effect calculations; Stata code is also updated. In addition, there are new chapters on methods for strengthening causal inference, including propensity scores, marginal structural models, and instrumental variables, and on methods for handling missing data, using maximum likelihood, multiple imputation, inverse weighting, and pattern mixture models. From the reviews of the first edition: "This book provides a unified introduction to the regression methods listed in the title...The methods are well illustrated by data drawn from medical studies...A real strength of this book is the careful discussion of issues common to all of the multipredictor methods covered." Journal of Biopharmaceutical Statistics, 2005 "This book is not just for biostatisticians. It is, in fact, a very good, and relatively nonmathematical, overview of multipredictor regression models. Although the examples are biologically oriented, they are generally easy to understand and follow...I heartily recommend the book" Technometrics, February 2006 "Overall, the text provides an overview of regression methods that is particularly strong in its breadth of coverage and emphasis on insight in place of mathematical detail. As intended, this well-unified approach should appeal to students who learn conceptually and verbally." Journal of the American Statistical Association, March 2006.
Note:
Description based upon print version of record
,
Regression Methods in Biostatistics; Preface; Preface to the First Edition; Contents; Chapter1 Introduction; 1.1 Example: Treatment of Back Pain; 1.2 The Family of Multipredictor Regression Methods; 1.3 Motivation for Multipredictor Regression; 1.3.1 Prediction; 1.3.2 Isolating the Effect of a Single Predictor; 1.3.3 Understanding Multiple Predictors; 1.4 Guide to the Book; Chapter2 Exploratory and Descriptive Methods; 2.1 Data Checking; 2.2 Types of Data; 2.3 One-Variable Descriptions; 2.3.1 Numerical Variables; 2.3.1.1 Example: Systolic Blood Pressure; 2.3.1.2 Numerical Description
,
2.3.1.3 Graphical Description2.3.1.4 Transformations of Data; 2.3.2 Categorical Variables; 2.4 Two-Variable Descriptions; 2.4.1 Outcome Versus Predictor Variables; 2.4.2 Continuous Outcome Variable; 2.4.2.1 Continuous Predictor; 2.4.2.2 Categorical Predictor; 2.4.3 Categorical Outcome Variable; 2.5 Multivariable Descriptions; 2.6 Summary; 2.7 Problems; Chapter3 Basic Statistical Methods; 3.1 t-Test and Analysis of Variance; 3.1.1 t-Test; 3.1.2 One- and Two-Sided Hypothesis Tests; 3.1.3 Paired t-Test; 3.1.4 One-Way Analysis of Variance; 3.1.5 Pairwise Comparisons in ANOVA
,
3.1.6 Multi-way ANOVA and ANCOVA3.1.7 Robustness to Violations of Normality Assumption; 3.1.8 Nonparametric Alternatives; 3.1.9 Equal Variance Assumption; 3.2 Correlation Coefficient; 3.2.1 Spearman Rank Correlation Coefficient; 3.2.2 Kendall's; 3.3 Simple Linear Regression Model; 3.3.1 Systematic Part of the Model; 3.3.2 Random Part of the Model; 3.3.3 Assumptions About the Predictor; 3.3.4 Ordinary Least Squares Estimation; 3.3.5 Fitted Values and Residuals; 3.3.6 Sums of Squares; 3.3.7 Standard Errors of the Regression Coefficients; 3.3.8 Hypothesis Tests and Confidence Intervals
,
3.3.8.1 Relationship Between Hypothesis Tests and Confidence Intervals3.3.8.2 Hypothesis Tests and Confidence Intervals in Large Samples; 3.3.9 Slope, Correlation Coefficient, and R2; 3.4 Contingency Table Methods for Binary Outcomes; 3.4.1 Measures of Risk and Association for Binary Outcomes; 3.4.2 Tests of Association in Contingency Tables; 3.4.3 Predictors with Multiple Categories; 3.4.4 Analyses Involving Multiple Categorical Predictors; 3.4.5 Collapsibility of Standard Measures of Association; 3.5 Basic Methods for Survival Analysis; 3.5.1 Right Censoring
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3.5.2 Kaplan-Meier Estimator of the Survival Function3.5.3 Interpretation of Kaplan-Meier Curves; 3.5.4 Median Survival; 3.5.5 Cumulative Event Function; 3.5.6 Comparing Groups Using the Logrank Test; 3.6 Bootstrap Confidence Intervals; 3.7 Interpretation of Negative Findings; 3.8 Further Notes and References; 3.9 Problems; 3.10 Learning Objectives; Chapter4 Linear Regression; 4.1 Example: Exercise and Glucose; 4.2 Multiple Linear Regression Model; 4.2.1 Systematic Part of the Model; 4.2.1.1 Interpretation of Adjusted Regression Coefficients; 4.2.2 Random Part of the Model
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4.2.2.1 Fitted Values, Sums of Squares, and Variance Estimators
Additional Edition:
ISBN 9781461413523
Additional Edition:
Buchausg. u.d.T. Vittinghoff, Eric Regression methods in biostatistics New York, NY : Springer, 2012 ISBN 9781461413523
Language:
English
Subjects:
Biology
,
Mathematics
Keywords:
Regressionsanalyse
;
Biostatistik
;
Biostatistik
;
Regressionsanalyse
DOI:
10.1007/978-1-4614-1353-0
URL:
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