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Biostatistics Decoded

Author:
A. Gouveia Oliveira
Publisher: Wiley
Copyright: 2013
ISBN-13: 978-1-119-95337-1
Pages: 246; paperback
Price: $49.75

Comment from the Stata technical group

Biostatistics Decoded is an introduction to biostatistics for medical professionals and clinical researchers. The book emphasizes concepts and basic calculations that will provide the reader with a foundation for understanding the study designs and statistical methods reported in the scientific literature.

The book is comprehensive and includes basic descriptive and inferential statistics as well as advanced topics such as the analysis of longitudinal studies, survival analysis, factor analysis, and meta analysis. A variety of study designs are also covered, including stratified and multistage sampling designs and modern experimental designs such as adaptive clinical trials and noninferiority trials.

The book avoids mathematical proofs and uses diagrams, graphs, and simulations to illustrate ideas. Familiarity with basic arithmetic, square roots, and logarithms is sufficient, and no knowledge of calculus is necessary. All examples are worked using Stata.


Table of contents

Preface
1 Introduction
1.1 The object of biostatistics
1.2 Defining the population
1.3 Study design
1.4 Sampling
1.5 Inferences from samples
2 Basic concepts
2.1 Data reduction
2.2 Scales of measurement
2.3 Tabulations of data
2.4 Central tendency measures
2.5 Measures of dispersion
2.6 Compressing data
2.7 The standard deviation
2.8 The n-1 divisor
2.9 Properties of means and variances
2.10 Common frequency distributions
2.11 The normal distribution
2.12 The central limit theorem
2.13 Properties of the normal distribution
2.14 Statistical tables
3 Statistical inference
3.1 Sampling distributions
3.2 The normal distribution of sample means
3.3 The standard error of the mean
3.4 The value of the standard error
3.5 Inferences from means
3.6 Confidence intervals
3.7 The case of small samples
3.8 Student’s t distribution
3.9 Statistical tables of the t distribution
3.10 Estimation with binary variables
3.11 The binomial distribution
3.12 Inferences from proportions
3.13 Statistical tables of the binomial distribution
3.14 Sample size requirements
4 Descriptive studies
4.1 Classification of descriptive studies
4.2 Probability sampling
4.3 Simple random sampling
4.4 Replacement in sampling
4.5 Stratified sampling
4.6 Multistage sampling
4.7 Prevalence studies
4.8 Incidence studies
4.9 The person-years method
4.10 Non-probability sampling in descriptive studies
4.11 Standardization
5 Analytical studies
5.1 Design of analytical studies
5.2 Non-probability sampling in analytical studies
5.3 The investigation of associations
5.4 Comparison of two means
5.5 Comparison of two means from small samples
5.6 Comparison of two proportions
5.7 Relative risks and odds ratios
5.8 Attributable risk
5.9 Logits and log odds ratios
6 Statistical tests
6.1 The null hypothesis
6.2 The z-test
6.3 The p-value
6.4 Student’s t-test
6.5 The binomial test
6.6 The chi-square test
6.7 Degrees of freedom
6.8 The table of the chi-square distribution
6.9 Analysis of variance
6.10 Statistical tables of the F distribution
7 Issues with statistical tests
7.1 One-sided tests
7.2 Power of a statistical test
7.3 Sample size estimation
7.4 Multiple comparisons
7.5 Scale transformation
7.6 Non-parametric tests
8 Longitudinal studies
8.1 Repeated measurements
8.2 The paired Student’s t-test
8.3 McNemar’s test
8.4 Analysis of events
8.5 The actuarial method
8.6 The Kaplan–Meier method
8.7 The logrank test
8.8 The adjusted logrank test
8.9 The Poisson distribution
8.10 The incidence rate ratio
9 Statistical modeling
9.1 Linear regression
9.2 The least squares method
9.3 Linear regression estimates
9.4 Regression and correlation
9.5 The F-test in linear regression
9.6 Interpretation of regression analysis results
9.7 Multiple regression
9.8 Regression diagnostics
9.9 Selection of predictor variables
9.10 Regression, t-test, and anova
9.11 Interaction
9.12 Nonlinear regression
9.13 Logistic regression
9.14 The method of maximum likelihood
9.15 Estimation of the logistic regression model
9.16 The likelihood ratio test
9.17 Interpreting the results of logistic regression
9.18 Regression coefficients and odds ratios
9.19 Applications of logistic regression
9.20 The ROC curve
9.21 Model validation
9.22 The Cox proportional hazards model
9.23 Assumptions of the Cox model
9.24 Interpretation of Cox regression
10 Measurement
10.1 Construction of clinical questionnaires
10.2 Factor analysis
10.3 Interpretation of factor analysis
10.4 Factor rotation
10.5 Factor scores
10.6 Reliability
10.7 Concordance
10.8 Validity
11 Experimental studies
11.1 The purpose of experimental studies
11.2 The clinical trial population
11.3 The efficacy criteria
11.4 Non-comparative clinical trials
11.5 Controlled clinical trials
11.6 Classical designs
11.7 The control group
11.8 Blinding
11.9 Randomization
11.10 The size of a clinical trial
11.11 Non-inferiority clinical trials
11.12 Adaptive clinical trials
11.13 Group sequential plans
11.14 The alpha spending function
11.15 The clinical trial protocol
11.16 The data record
12 The analysis of experimental studies
12.1 General analysis plan
12.2 Data preparation
12.3 Study populations
12.4 Primary efficacy analysis
12.5 Analysis of multiple endpoints
12.6 Secondary analyses
12.7 Safety analysis
13 Meta-analysis of clinical trials
13.1 Purpose of meta-analysis
13.2 Measures of treatment effect
13.3 The inverse variance method
13.4 The random effects model
13.5 Heterogeneity
13.6 Publication bias
13.7 Presentation of results
Further reading
Index
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