Power and Sample Size in R

Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
Preamble
List of Figures
List of Tables
1. Preliminaries
	1.1. R implementation
	1.2. Probability distributions
	1.3. Notation for common distributions
	1.4. R functions for common distributions
	1.5. Symmetric property of the normal distribution
	1.6. Standardizing a normal distribution
2. Getting started: a first calculation
	2.1. Steps in a sample size calculation
	2.2. Hypothesis testing
	2.3. A first calculation: one-sample z test
	2.4. Effect size
	2.5. Minimum detectable effect size
	2.6. A general formula when the test statistic is normally distributed
	2.7. R function for z tests
	2.8. Sample size adjustments
	2.9. Sensitivity analysis
	2.10. Estimating power using simulation
	2.11. Should I conduct a power analysis after my study is completed?
3. One or two means
	3.1. One-sample t test
	3.2. Two independent samples t test
	3.3. Relative efficiency
	3.4. Lognormal data
	3.5. Paired t test
	3.6. Remarks on R functions for t tests
	3.7. Nonparametric tests of location
4. Hypotheses for different study objectives
	4.1. Introduction
	4.2. Test for nonequality
	4.3. Test for superiority
	4.4. Test for noninferiority
	4.5. Test for superiority by a margin
	4.6. Test for equivalence
	4.7. Hypotheses when a lower mean corresponds to a better outcome
	4.8. Remarks
5. Analysis of variance for comparing means
	5.1. Introduction
	5.2. One-way analysis of variance
	5.3. Two-way analysis of variance
	5.4. Analysis of covariance
	5.5. Additional resources
6. Proportions: large sample methods
	6.1. Preliminaries
	6.2. One-sample proportion test
	6.3. Test of two independent proportions
	6.4. Test for two correlated proportions
7. Exact methods for proportions
	7.1. One proportion: exact binomial test
	7.2. Two-stage designs for single arm trials
	7.3. Two proportions: Fisher exact test
	7.4. Two correlated proportions: exact test
8. Categorical variables
	8.1. Chi-square goodness-of-fit test
	8.2. Chi-square test of independence
	8.3. Chi-square test for comparing two proportions
	8.4. Ordinal categorical responses
	8.5. Additional resources
9. Precision and confidence intervals
	9.1. Introduction
	9.2. Confidence intervals for means
	9.3. Confidence intervals for proportions
	9.4. Confidence intervals for relative risk
	9.5. Confidence intervals for odds ratio
	9.6. Additional resources
10. Correlation and linear regression
	10.1. Pearson correlation coefficient
	10.2. Simple linear regression
	10.3. Multiple linear regression
11. Generalized linear regression
	11.1. Power for generalized linear models
	11.2. Logistic regression
	11.3. Poisson regression
	11.4. Additional resources
12. Crossover studies
	12.1. Introduction
	12.2. 2. × 2. crossover design
	12.3. (2. × 2)r crossover design
	12.4. Efficiency of crossover designs
	12.5. Additional resources
13. Multisite trials
	13.1. Introduction
	13.2. Multilevel data structure
	13.3. Considerations for multisite trials
	13.4. Model for continuous outcomes
	13.5. Intraclass correlation coefficient
	13.6. Power for test of average treatment effect
	13.7. Power for test of heterogeneity of treatment effect
	13.8. Binary outcomes
	13.9. Additional resources
14. Cluster randomized trials: parallel designs
	14.1. Introduction
	14.2. Continuous outcomes
	14.3. Binary outcomes
	14.4. Additional resources for parallel cluster randomized trials
	14.5. Individually randomized group treatment trials
	14.6. Other multilevel trial designs
15. Cluster randomized trials: longitudinal designs
	15.1. Introduction
	15.2. Modeling framework for continuous outcomes
	15.3. Parallel cluster randomized trial with baseline measurement
	15.4. Cluster randomized crossover designs
	15.5. Stepped wedge designs
16. Time to event outcomes
	16.1. Introduction
	16.2. Concepts for time to event studies
	16.3. Logrank test
	16.4. Tests based on the Kaplan-Meier estimator
	16.5. Distributions for survival, accrual and loss to follow up
	16.6. Additional resources
17. Multiple primary endpoints
	17.1. Introduction
	17.2. Model
	17.3. Co-primary endpoints
	17.4. Alternative primary endpoints
	17.5. Additional resources
Bibliography
Index