Details

Autor(en) / Beteiligte

• Campbell, Michael J.,
• Jacques, Richard M.,

Titel

Statistics at square two : understanding modern statistical applications in medicine

Auflage

Third edition

Ort / Verlag

Hoboken, New Jersey : John Wiley & Sons Ltd,

Erscheinungsjahr

[2023]

Link zum Volltext

• Wiley Online Library - AutoHoldings Books

Beschreibungen/Notizen

• Includes index.
• Intro -- Title page -- Copyright -- Table of Contents -- Preface -- 1 Models, Tests and Data -- 1.1 Types of Data -- 1.2 Confounding, Mediation and Effect Modification -- 1.3 Causal Inference -- 1.4 Statistical Models -- 1.5 Results of Fitting Models -- 1.6 Significance Tests -- 1.7 Confidence Intervals -- 1.8 Statistical Tests Using Models -- 1.9 Many Variables -- 1.10 Model Fitting and Analysis: Exploratory and Confirmatory Analyses -- 1.11 Computer-intensive Methods -- 1.12 Missing Values -- 1.13 Bayesian Methods -- 1.14 Causal Modelling -- 1.15 Reporting Statistical Results in the Medical Literature -- 1.16 Reading Statistics in the Medical Literature -- 2 Multiple Linear Regression -- 2.1 The Model -- 2.2 Uses of Multiple Regression -- 2.3 Two Independent Variables -- 2.3.1 One Continuous and One Binary Independent Variable -- 2.3.2 Two Continuous Independent Variables -- 2.3.3 Categorical Independent Variables -- 2.4 Interpreting a Computer Output -- 2.4.1 One Continuous Variable -- 2.4.2 One Continuous Variable and One Binary Independent Variable -- 2.4.3 One Continuous Variable and One Binary Independent Variable with Their Interaction -- 2.4.4 Two Independent Variables: Both Continuous -- 2.4.5 Categorical Independent Variables -- 2.5 Examples in the Medical Literature -- 2.5.1 Analysis of Covariance: One Binary and One Continuous Independent Variable -- 2.5.2 Two Continuous Independent Variables -- 2.6 Assumptions Underlying the Models -- 2.7 Model Sensitivity -- 2.7.1 Residuals, Leverage and Influence -- 2.7.2 Computer Analysis: Model Checking and Sensitivity -- 2.8 Stepwise Regression -- 2.9 Reporting the Results of a Multiple Regression -- 2.10 Reading about the Results of a Multiple Regression -- 2.11 Frequently Asked Questions -- 2.12 Exercises: Reading the Literature -- 3 Multiple Logistic Regression -- 3.1 Quick Revision.
• 3.2 The Model -- 3.2.1 Categorical Covariates -- 3.3 Model Checking -- 3.3.1 Lack of Fit -- 3.3.2 "Extra-binomial" Variation or "Over Dispersion" -- 3.3.3 The Logistic Transform is Inappropriate -- 3.4 Uses of Logistic Regression -- 3.5 Interpreting a Computer Output -- 3.5.1 One Binary Independent Variable -- 3.5.2 Two Binary Independent Variables -- 3.5.3 Two Continuous Independent Variables -- 3.6 Examples in the Medical Literature -- 3.6.1 Comment -- 3.7 Case-control Studies -- 3.8 Interpreting Computer Output: Unmatched Case-control Study -- 3.9 Matched Case-control Studies -- 3.10 Interpreting Computer Output: Matched Case-control Study -- 3.11 Example of Conditional Logistic Regression in the Medical Literature -- 3.11.1 Comment -- 3.12 Alternatives to Logistic Regression -- 3.13 Reporting the Results of Logistic Regression -- 3.14 Reading about the Results of Logistic Regression -- 3.15 Frequently Asked Questions -- 3.16 Exercise -- 4 Survival Analysis -- 4.1 Introduction -- 4.2 The Model -- 4.3 Uses of Cox Regression -- 4.4 Interpreting a Computer Output -- 4.5 Interpretation of the Model -- 4.6 Generalisations of the Model -- 4.6.1 Stratified Models -- 4.6.2 Time Dependent Covariates -- 4.6.3 Parametric Survival Models -- 4.6.4 Competing Risks -- 4.7 Model Checking -- 4.8 Reporting the Results of a Survival Analysis -- 4.9 Reading about the Results of a Survival Analysis -- 4.10 Example in the Medical Literature -- 4.10.1 Comment -- 4.11 Frequently Asked Questions -- 4.12 Exercises -- 5 Random Effects Models -- 5.1 Introduction -- 5.2 Models for Random Effects -- 5.3 Random vs Fixed Effects -- 5.4 Use of Random Effects Models -- 5.4.1 Cluster Randomised Trials -- 5.4.2 Repeated Measures -- 5.4.3 Sample Surveys -- 5.4.4 Multi-centre Trials -- 5.5 Ordinary Least Squares at the Group Level -- 5.6 Interpreting a Computer Output.
• 5.6.1 Different Methods of Analysis -- 5.6.2 Likelihood and gee -- 5.6.3 Interpreting Computer Output -- 5.7 Model Checking -- 5.8 Reporting the Results of Random Effects Analysis -- 5.9 Reading about the Results of Random Effects Analysis -- 5.10 Examples of Random Effects Models in the Medical Literature -- 5.10.1 Cluster Trials -- 5.10.2 Repeated Measures -- 5.10.3 Comment -- 5.10.4 Clustering in a Cohort Study -- 5.10.5 Comment -- 5.11 Frequently Asked Questions -- 5.12 Exercises -- 6 Poisson and Ordinal Regression -- 6.1 Poisson Regression -- 6.2 The Poisson Model -- 6.3 Interpreting a Computer Output: Poisson Regression -- 6.4 Model Checking for Poisson Regression -- 6.5 Extensions to Poisson Regression -- 6.6 Poisson Regression Used to Estimate Relative Risks from a 2 × 2 Table -- 6.7 Poisson Regression in the Medical Literature -- 6.8 Ordinal Regression -- 6.9 Interpreting a Computer Output: Ordinal Regression -- 6.10 Model Checking for Ordinal Regression -- 6.11 Ordinal Regression in the Medical Literature -- 6.12 Reporting the Results of Poisson or Ordinal Regression -- 6.13 Reading about the Results of Poisson or Ordinal Regression -- 6.14 Frequently Asked Question -- 6.15 Exercises -- 7 Meta-analysis -- 7.1 Introduction -- 7.2 Models for Meta-analysis -- 7.3 Missing Values -- 7.4 Displaying the Results of a Meta-analysis -- 7.5 Interpreting a Computer Output -- 7.6 Examples from the Medical Literature -- 7.6.1 Example of a Meta-analysis of Clinical Trials -- 7.6.2 Example of a Meta-analysis of Case-control Studies -- 7.7 Reporting the Results of a Meta-analysis -- 7.8 Reading about the Results of a Meta-analysis -- 7.9 Frequently Asked Questions -- 7.10 Exercise -- 8 Time Series Regression -- 8.1 Introduction -- 8.2 The Model -- 8.3 Estimation Using Correlated Residuals -- 8.4 Interpreting a Computer Output: Time Series Regression.
• 8.5 Example of Time Series Regression in the Medical Literature -- 8.6 Reporting the Results of Time Series Regression -- 8.7 Reading about the Results of Time Series Regression -- 8.8 Frequently Asked Questions -- 8.9 Exercise -- Appendix 1 Exponentials and Logarithms -- Appendix 2 Maximum Likelihood and Significance Tests -- A2.1 Binomial Models and Likelihood -- A2.2 The Poisson Model -- A2.3 The Normal Model -- A2.4 Hypothesis Testing: the Likelihood Ratio Test -- A2.5 The Wald Test -- A2.6 The Score Test -- A2.7 Which Method to Choose? -- A2.8 Confidence Intervals -- A2.9 Deviance Residuals for Binary Data -- A2.10 Example: Derivation of the Deviances and Deviance Residuals Given in Table 3.3 -- A2.10.1 Grouped Data -- A2.10.2 Ungrouped Data -- Appendix 3 Bootstrapping and Variance Robust Standard Errors -- A3.1 The Bootstrap -- A3.2 Example of the Bootstrap -- A3.3 Interpreting a Computer Output: The Bootstrap -- A3.3.1 Two-sample T-test with Unequal Variances -- A3.4 The Bootstrap in the Medical Literature -- A3.5 Robust or Sandwich Estimate SEs -- A3.6 Interpreting a Computer Output: Robust SEs for Unequal Variances -- A3.7 Other Uses of Robust Regression -- A3.8 Reporting the Bootstrap and Robust SEs in the Literature -- A3.9 Frequently Asked Question -- Appendix 4 Bayesian Methods -- A4.1 Bayes' Theorem -- A4.2 Uses of Bayesian Methods -- A4.3 Computing in Bayes -- A4.4 Reading and Reporting Bayesian Methods in the Literature -- A4.5 Reading about the Results of Bayesian Methods in the Medical Literature -- Appendix 5 R codes -- A5.1 R Code for Chapter 2 -- A5.3 R Code for Chapter 3 -- A5.4 R Code for Chapter 4 -- A5.5 R Code for Chapter 5 -- A5.6 R Code for Chapter 6 -- A5.7 R Code for Chapter 7 -- A5.8 R Code for Chapter 8 -- A5.9 R Code for Appendix 1 -- A5.10 R Code for Appendix 2 -- A5.11 R Code for Appendix 3 -- Answers to Exercises.
• Glossary -- Index -- End User License Agreement.
• Description based on print version record.