Biostatistics for clinical and public health research

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Acknowledgments
Author
List of abbreviations
Introduction
	General overview
		What is statistics?
		The two main branches of statistics
		Basic problem of statistics
		Data
		Units and variables
			Types of variables
	References
Chapter 1: Descriptive statistics
	Terms
	Introduction
	Measures of central tendency (Measures of location)
	Example Problem 1.1
	Practice Problem 1.1
		Arithmetic mean versus median
			Arithmetic mean
			Median
	Example of geometric mean using data from the 2014 National Health and Nutrition Examination Survey
	Example Problem 1.2
	Practice Problem 1.2
	Measures of spread
	Example Problem 1.3
	Practice Problem 1.3
	Measures of variability
	Example Problem 1.4
	Practice Problem 1.4
		Grouped mean
		Grouped variance
	Example Problem 1.5
	Practice Problem 1.5
	Types of graphs
		What makes a good graphic or tabular display?
	Example Problem 1.6
	Practice Problem 1.6
	Outliers and standard distribution rules
	Practice Problem 1.7
	Practice Problem 1.8
	Practice Problem 1.9
	Practice Problem 1.10
	References
Lab A: Introduction to SAS®
	The basics
	Getting started
	What is SAS?
		SAS windowing environment
			SAS menus
			SAS programs
			Opening data files in SAS
			Data editor
		Inputting data into SAS
		Labels
		Summarizing data
	Example Problem A.1
	Formats
	Printing SAS output
	Creating new variables with formulas
	Graphs
	Example Problem A.2
		Saving graphs
	Practice Problem A.1
	Practice Problem A.2
	Practice Problem A.3
	References
Lab A: Introduction to Stata®
	The basics
	Getting started
		What is Stata?
			Stata Windows
			Stata menus
			Opening existing Stata data files
			The Do Editor
			Data Editor
			Inputting data into Stata
			Labels
			Log files
			Summarizing data
	Example Problem A.1
	Value labels
	Printing
	Creating new variables with formulas
	Graphs
	Example Problem A.2
		Saving graphs
	Practice Problem A.1
	Practice Problem A.2
	Practice Problem A.3
	References
Chapter 2: Probability
	Terms
	Introduction
		Example of probability in action
	Properties of probabilities
	Laws of probability
	Mutually exclusive and exhaustive
	References
Chapter 3: Diagnostic testing/screening
	Terms
	Introduction
	Diagnostic terms and concepts
	Receiver operating characteristic curves
	References
Chapter 4: Discrete probability distributions
	Terms
	Introduction
	Examples of discrete random variables
	Examples of continuous random variables
	Measures of location and spread for random variables
	Permutations and combinations
	Binomial distribution
		Properties of the binomial distribution
		Parameters of binomial distribution
	Poisson distribution
		Poisson distribution properties
		Poisson distribution estimation
	References
Chapter 5: Continuous probability distributions
	Terms
	Introduction
	Distribution functions
	Normal distribution
		Standard normal distribution
		Standardization of a normal variable
		Why use the standard normal?
	Review of probability distributions
	References
Lab B: Probability distributions
	References
Chapter 6: Estimation
	Terms
	Introduction
	Statistical inference
	Sampling
	Randomized clinical trials
		Types of clinical trials
	Population and sample mean
		Sampling distribution of the mean
		Unbiased estimators
		Population and sample measures of spread
		Central limit theorem
	Example Problem 6.2
	Confidence intervals for means
		What the confidence interval does not imply
		What the confidence interval does imply
		Example demonstrating the meaning of confidence interval
		Hand calculation of one-sided confidence interval
		Width of confidence interval
	Using the standard normal distribution for a mean
		Using SAS to construct confidence intervals for a mean
			The PROC MEANS procedure
		Using Stata to construct confidence intervals for a mean
		Requesting a one-sided confidence interval
	The t-distribution
	Obtaining critical values in SAS and Stata
	Sampling distribution for proportions
	Confidence intervals for proportions
		Using SAS to obtain confidence intervals for proportions
		Using Stata to obtain confidence intervals for proportions
	References
Chapter 7: One-sample hypothesis testing
	Terms
	Introduction
	Basics of hypothesis testing
		Step 1: State the hypothesis
		Step 2: Specify the significance level
		Step 3: Draw sample of size n
		Step 4: Compute the test statistic
		Step 5: Compare p-value to α and determine to reject or fail to reject the null hypothesis
			Types of errors
			Asymmetry of errors
		Step 6: State conclusions regarding subject matter
	Confidence intervals and hypothesis tests
		One-sample tests for the mean using software
			One-sample tests for the mean using SAS
			One-sample tests for the mean using Stata
	Inference for proportions
		Hypothesis testing for proportions
		One-sample tests for a proportion using SAS
		One-sample tests for a proportion using Stata
	Determining power and calculating sample size
		Calculating power
			10 steps to finding the power of a one-sample, one-sided test
		Calculating sample size
		Approximate sample size based on confidence interval width
		Power and sample size for one-sample tests for the mean using SAS
		Power and sample size for one-sample tests for the mean using Stata
		Power and sample size for one-sample tests for a proportion using SAS
		Power and sample size for one-sample tests for a proportion using Stata
	References
Lab C: One-sample hypothesis testing, power, and sample size
	References
Chapter 8: Two-Sample Hypothesis Testing
	Terms
	Introduction
	Dependent samples (paired tests)
		Using SAS with dependent samples
		Using Stata with dependent samples
	Independent samples
		Testing for the equality of two variances
			Test for equality of variances in stata
			t-Test with equal variances in SAS
			t-Test with equal variances in Stata
			t-Test with unequal variances in SAS
			t-Test with unequal variances in Stata
	Sample size and power for two-sample test of means
		Using SAS for sample size and power for two-sample test of means
		Using Stata for sample size and power for two-sample test of means
	PRACTICE PROBLEM 8.3
	References
Chapter 9: Nonparametric hypothesis testing
	Terms
	Introduction
	Types of data
	Parametric vs. nonparametric tests
		Nonparametric tests for paired data
			Sign test
			Wilcoxon signed-rank test
			Performing nonparametric tests for paired data in SAS
			Performing nonparametric tests for paired data in Stata
		Nonparametric tests for independent data
			Wilcoxon rank-sum test
			Performing nonparametric tests for independent data in SAS
			Performing nonparametric tests for independent data in Stata
	References
Lab D: Two-sample (parametric and nonparametric) hypothesis testing
	Practice Problem D.1
	Practice Problem D.2
	Practice Problem D.3
	Practice Problem D.4
	Practice Problem D.5
	References
Chapter 10: Hypothesis testing with categorical data
	Terms
	Introduction
	Two-sample test for proportions
		Normal theory method
	Example Problem 10.1
	Practice Problem 10.1
		Contingency table methods
			2 × 2 Contingency table
			R × C contingency table
			The chi-squared distribution
			Sample size and power for comparing two binomial proportions
			Sample size and power for two binomial proportions in SAS
			Sample size and power for two binomial proportions in stata
			Sample size and power in a clinical trial setting
	Example Problem 10.2
	Practice Problem 10.2
	Example Problem 10.3
	Example Problem 10.4
	Practice Problem 10.3
	Example Problem 10.5
	Practice Problem 10.4
	Practice Problem 10.5
	Practice Problem 10.6
	References
Chapter 11: Analysis of variance (ANOVA)
	Terms
	Introduction
	Within- and between-group variation
	ANOVA assumptions
		Independence
		Normality
		Equal variance
	Testing for significance
		Multiple comparisons
		Using SAS to conduct an ANOVA
			Homogeneity of variance in SAS
			Multiple comparisons in SAS
		Using Stata to conduct an ANOVA
			Multiple comparisons in stata
	References
Chapter 12: Correlation
	Term
	Introduction
	Population correlation coefficient (ρ)
		Visualizing correlated data
	Pearson correlation coefficient (r)
		Using SAS to calculate the Pearson correlation coefficient
		Testing a different “Null” hypothesis about the Pearson correlation coefficient, using SAS
		Using Stata to calculate the Pearson correlation coefficient
	Spearman rank correlation coefficient (rs)
		Using SAS to calculate Spearman rank correlation coefficient
		Using Stata to calculate Spearman rank correlation coefficient
	References
Chapter 13: Linear regression
	Simple linear regression
		Regression concepts
		Method of least squares
		Linear relationship
		Inference for predicted values
			Predicting a mean outcome value
			Predicting an individual outcome value
		Evaluation of the model
	Multiple linear regression
	Model evaluation
	Other explanatory variables
		Indicator variables
		Categorical variables
		Interaction terms
	Model selection
		Collinearity
	References
Chapter 14: Logistic regression
	Term
	Introduction
	Interpretation of coefficients
		Hypothesis tests and confidence intervals for estimated regression coefficients
		Model evaluation
			Logistic regression in SAS
			Logistic regression in Stata
	References
Chapter 15: Survival analysis
	Terms
	Introduction
	Comparing two survival functions
	References
Lab E: Data analysis project
	Part one
	Part two
	Part three
	Part four
	Part five
	Part six
	Part seven
	Part eight
	Part nine
	Part ten
	Reference
Appendix: Statistical tables
Index