Medical Statistics: A Textbook for the Health Sciences, Fourth Edition |
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Comment from the Stata technical groupMedical Statistics: A Textbook for the Health Sciences, Fourth Edition, by Michael J. Campbell, David Machin, and Stephen J. Walters, is one of many texts suitable for a semester-long statistics course for health professionals. However, it is both thorough and easy to read, setting it apart from other texts. The authors include the standard tools relevant to health professionals: odds ratios, survival analysis, observational studies, and more. Another helpful feature in this text is a section at the end of each chapter listing points the reader should consider when reading research publications about the use of the discussed methods. |
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Table of contentsView table of contents >>
Preface
Chapter 1 Uses and abuses of medical statistics
1.1 Introduction
1.2 Why use statistics? 1.3 Statistics is about common sense and good design 1.4 Types of data 1.5 How a statistician can help 1.6 Further reading 1.7 Exercises
Chapter 2 Describing and displaying categorical data
2.1 Summarising categorical data
2.2 Displaying categorical data 2.3 Points when reading the literature 2.4 Exercises
Chapter 3 Describing and displaying quantitative data
3.1 Summarising continuous data
3.2 Displaying continuous data 3.3 Within-subject variability 3.4 Presentation 3.5 Points when reading the literature 3.6 Exercises
Chapter 4 Probability and decision making
4.1 Types of probability
4.2 Diagnostic tests 4.3 Bayes’ Theorem 4.4 Relative (receiver)-operating characteristic (ROC) curve 4.5 Points when reading the literature 4.6 Exercises
Chapter 5 Distributions
5.1 Introduction
5.2 The Binomial distribution 5.3 The Poisson distribution 5.4 Probability for continuous outcomes 5.5 The Normal distribution 5.6 Reference ranges 5.7 Points when reading the literature 5.8 Technical details 5.9 Exercises
Chapter 6 Populations, samples, standard errors and confidence intervals
6.1 Populations
6.2 Samples 6.3 The standard error 6.4 The Central Limit Theorem 6.5 Standard errors for proportions and rates 6.6 Standard errors of differences 6.7 Confidence intervals for an estimate 6.8 Confidence intervals for differences 6.9 Points when reading the literature 6.10 Technical details 6.11 Exercises
Chapter 7 p-Values and statistical inference
7.1 Introduction
7.2 The null hypothesis 7.3 The p-value 7.4 Statistical inference 7.5 Statistical power 7.6 Confidence intervals rather than p-values 7.7 One-sided and two-sided tests 7.8 Points when reading the literature 7.9 Technical details 7.10 Exercises
Chapter 8 Tests for comparing two groups of categorical or continuous data
8.1 Introduction
8.2 Comparison of two groups of paired observations—continuous outcomes 8.3 Comparison of two independent groups—continuous outcomes 8.4 Comparison of two independent groups—categorical outcomes 8.5 Comparison of two groups of paired observations—categorical outcomes 8.6 Non-Normal distributions 8.7 Degrees of freedom 8.8 Points when reading the literature 8.9 Technical details 8.10 Exercises
Chapter 9 Correlation and linear regression
9.1 Introduction
9.2 Correlation 9.3 Linear regression 9.4 Comparison of assumptions between correlation and regression 9.5 Multiple regression 9.6 Logistic regression 9.7 Correlation is not causation 9.8 Points when reading the literature 9.9 Technical details 9.10 Exercises
Chapter 10 Survival analysis
10.1 Time to event data
10.2 Kaplan-Meier survival curve 10.3 The logrank test 10.4 The hazard ratio 10.5 Modelling time to event data 10.6 Points when reading the literature 10.7 Exercises
Chapter 11 Reliability and method comparison studies
11.1 Introduction
11.2 Repeatability 11.3 Agreement 11.4 Validity 11.5 Method comparison studies 11.6 Points when reading the literature 11.7 Technical details 11.8 Exercises
Chapter 12 Observational studies
12.1 Introduction
12.2 Risk and rates 12.3 Taking a random sample 12.4 Questionnaire and form design 12.5 Cross-sectional surveys 12.6 Non-randomised studies 12.7 Cohort studies 12.8 Case-control studies 12.9 Association and causality 12.10 Points when reading the literature 12.11 Technical details 12.12 Exercises
Chapter 13 The randomised controlled trial
13.1 Introduction
13.2 Why randomise? 13.3 Methods of randomisation 13.4 Design features 13.5 Design options 13.6 Meta-analysis 13.7 The protocol 13.8 Checklists for design, analysis and reporting 13.9 Number needed to treat (NNT) 13.10 Points when reading the literature 13.11 Exercises
Chapter 14 Sample size issues
14.1 Introduction
14.2 Study size 14.3 Continuous data 14.4 Binary data 14.5 Prevalence 14.6 Subject withdrawals 14.7 Internal pilot studies 14.8 Points when reading the literature 14.9 Technical details 14.10 Exercises
Chapter 15 Common pitfalls
15.1 Introduction
15.2 Using the t-test 15.3 Plotting change against initial value 15.4 Repeated measures 15.5 Clinical and statistical significance 15.6 Exploratory data analysis 15.7 Points when reading the literature 15.8 Exercises
References
Solutions to exercises
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