 |
| |||||
 |
| >> Home >> Bookstore >> Statistics >> Biostatistics and epidemiology >> Epidemiology: Study Design and Data Analysis, 2nd Edition | |||||||||||||||||||||
Epidemiology: Study Design and Data Analysis, 2nd Edition
Comments from the Stata technical groupWoodward’s second edition of Epidemiology: Study Design and Data Analysis has two target audiences: researchers who need statistical solutions to epidemiology problems and statisticians who wish to learn how their science applies to epidemiology. Fulfilling these goals requires careful navigation through a narrow course that is neither too theoretical nor too overcome by medical jargon. This book not only successfully navigates this course but does so while providing a complete treatment of the topic, all the way from simple contingency tables to meta-analysis. The book uses real data throughout—more than 20 large datasets are cataloged for download—and the end of each chapter has exercises (with solutions). Woodward makes Stata code for working many of the examples available for download. Topics covered include basic terminology, causality, descriptive statistics, testing of means, relative risks versus odds ratios, exact tests based on tables, tests for linear and nonlinear trends, confounding and interaction including direct and indirect standardization, cohort designs, case–control studies, intervention studies, power and sample size, linear models including analysis of variance, logistic and other models for binary responses, survival analysis including Cox regression, and meta-analysis. Table of contents
1. Fundamental issues
1.1 What is epidemiology?
1.2 Case studies: the work of Doll and Hill 1.3 Populations and samples
1.3.1 Populations
1.4 Measuring disease
1.3.2 Samples
1.4.1 Incidence and prevalence
1.5 Measuring the risk factor 1.6 Causality
1.6.1 Association
1.7 Studies using routine data
1.6.2 Problems with establishing causality 1.6.3 Principals of causality
1.7.1 Ecological data
1.8 Study design
1.7.2 Sources of data on disease 1.7.3 Sources of data on risk factors
1.8.1 Intervention studies
1.9 Data analysis 1.8.2 Observational studies Exercises
2. Basic analytical procedures
2.1 Introduction
2.1.1 Inferential procedures
2.2 Case study
2.2.1 The Scottish Heart Health Study
2.3 Types of variables
2.3.1 Qualitative variables
2.4 Tables and charts
2.3.2 Quantitative variables 2.3.3 The hierarchy of type
2.4.1 Tables in reports
2.5 Inferential techniques for categorical variables
2.4.2 Diagrams in reports
2.5.1 Contingency tables
2.6 Descriptive techniques for quantitative variables
2.5.2 Binary variables: proportions and percentages 2.5.3 Comparing two proportions or percentages
2.6.1 The five-number summary
2.7 Inferences about means
2.6.2 Quantiles 2.6.3 The two-number summary 2.6.4 Other summary statistics of spread 2.6.5 Assessing symmetry 2.6.6 Investigating shape
2.7.1 Checking normality
2.8 Inferential techniques for non-normal data
2.7.2 Inferences for a single mean 2.7.3 Comparing two means 2.7.4 Paired data
2.8.1 Transformations
2.9 Measuring agreement
2.8.2 Nonparametric tests 2.8.3 Confidence intervals for medians
2.9.1 Quantitative variables
2.10 Assessing diagnostic tests
2.9.2 Categorical variables 2.9.3 Ordered categorical variables
2.10.1 Accounting for sensitivity and specificity
Exercises
3. Assessing risk factors
3.1 Risk and relative risk
3.2 Odds and odds ratios 3.3 Relative risk or odds ratio? 3.4 Prevalence studies 3.5 Testing association
3.5.1 Equivalent tests
3.6 Risk factors at several levels
3.5.2 One-sided tests 3.5.3 Continuity corrections 3.5.4 Fisher's exact test 3.5.5 Limitations of tests
3.6.1 Continuous risk factors
3.7 Attributable risk 3.6.2 A test for linear trend 3.6.3 A test for nonlinearity 3.8 Rate and relative rate
3.8.1 The general epidemiological rate
3.9 Measures of difference Exercises
4. Confounding and interaction
4.1 Introduction
4.2 The concept of confounding 4.3 Identification of confounders
4.3.1 A strategy for selection
4.4 Assessing confounding
4.4.1 Using estimation
4.5 Stadardisation
4.4.2 Using hypothesis tests 4.4.3 Dealing with several confounding variables
4.5.1 Direct standardisation of event rates
4.6 Mantel–Haenszel methods
4.5.2 Indirect standardisation of event rates 4.5.3 Standardisation of risks
4.6.1 The Mantel–Haenszel relative risk
4.7 The concept of interaction 4.6.2 The Cochran–Mantel–Haenszel test 4.6.3 Further comments 4.8 Testing for interaction
4.8.1 Using relative risk
4.9 Dealing with interaction 4.8.2 Using the odds ratio 4.8.3 Using the risk difference 4.8.4 Which type of interaction to use? 4.8.5 Which interactions to test? Exercises
5. Cohort studies
5.1 Design considerations
5.1.1 Advantages
5.2 Analytical considerations
5.1.2 Disadvantages 5.1.3 Alternative designs with economic advantages 5.1.4 Studies with a single baseline examples
5.2.1 Concurrent follow-up
5.3 Cohort life tables
5.2.2 Moving baseline dates 5.2.3 Varying follow-up durations 5.2.4 Withdrawals 5.2.5 Competing causes of failure
5.3.1 Allowing for sampling variation
5.4 Kaplan–Meier estimation
5.3.2 Allowing for censoring 5.3.3 Comparison of two life tables 5.3.4 Limitations
5.4.1 An empirical comparison
5.5 Comparison of two sets of survival probabilities
5.5.1 Mantel–Haenszel methods
5.6 The person-years method
5.5.2 The log-rank test 5.5.3 Weighted log-rank tests 5.5.4 Allowing for confounding variables 5.5.5 Comparing three or more groups
5.6.1 Age-specific rates
5.7 Period-cohort analysis
5.6.2 Summarization of rates 5.6.3 Comparison of two SERs 5.6.4 Mantel–Haenszel methods 5.6.5 Further comments
5.7.1 Period-specific rates
Exercises
6. Case–control studies
6.1 Basic design concepts
6.1.1 Advantages
6.2 Basic methods of analysis
6.1.2 Disadvantages
6.2.1 Dichotomous exposure
6.3 Selection of cases
6.2.2 Polytomous exposure 6.2.3 Confounding and interaction 6.2.4 Attributable risk
6.3.1 Definition
6.4 Selection of controls
6.3.2 Inclusion and exclusion criteria 6.3.3 Incident or prevalent? 6.3.4 Source 6.4.5 Consideration of bias
6.4.1 General principles
6.5 Matching
6.4.2 Hospital controls 6.4.3 Community controls 6.4.5 Other sources 6.4.6 How many?
6.5.1 Advantages
6.6 The analysis of matched studies
6.5.2 Disadvantages 6.5.3 One-to-many matching 6.5.4 Matching in other study designs
6.6.1 1 : 1 Matching
6.7 Nested case–control studies6.6.2 1 : c Matching 6.6.3 1 : Variable matching 6.6.4 Many : many matching 6.6.5 A modelling approach
6.7.1 Matched studies
6.8 Case–cohort studies6.7.2 Counter-matched studies 6.9 Case–crossover studies Exercises
7. Intervention studies
7.1 Introduction
7.1.1 Advantages
7.2 Ethical considerations
7.1.2 Disadvantages
7.2.1 The protocol
7.3 Avoidance of bias
7.3.1 Use of a control group
7.4 Parallel group studies
7.3.2 Blindness 7.3.3 Randomization 7.3.4 Consent before randomization 7.3.5 Analysis by intention-to-treat
7.4.1 Number needed to treat
7.5 Cross-over studies
7.4.2 Cluster randomized trials
7.5.1 Graphical analysis
7.6 Sequential studies7.5.2 Comparing means 7.5.3 Analysing preferences 7.5.4 Analysing binary data 7.7 Allocation to treatment group
7.7.1 Global randomization
Exercises
7.7.2 Stratified randomization 7.7.3 Implementation
8. Sample size determination
8.1 Introduction
8.2 Power
8.2.1 Choice of alternative hypothesis
8.3 Testing a mean value
8.3.1 Common choices for power and significance level
8.4 Testing a difference between means
8.3.2 Using a table of sample sizes 8.3.3 The minimum detectable difference 8.3.4 The assumption of known standard deviation
8.4.1 Using a table of sample sizes
8.5 Testing a proportion
8.4.2 Power and minimum detectable difference 8.4.3 Optimum distribution of the sample 8.4.4 Paired data
8.5.1 Using a table of sample sizes
8.6 Testing a relative risk
8.5.2 Power and minimum detectable difference
8.6.1 Using a table of sample sizes
8.7 Case–control studies
8.6.2 Power and minimum detectable relative risk
8.7.1 Using a table of samples sizes
8.8 Complex sampling designs8.7.2 Power and minimum detectable relative risk 8.7.3 Comparison with cohort studies 8.7.4 Matched studies 8.9 Concluding remarks Exercises
9. Modelling quantitative outcome variables
9.1 Statistical models
9.2 One categorical explanatory variable
9.2.1 The hypotheses to be tested
9.3 One quantitative explanatory variable
9.2.2 Construction of the ANOVA table 9.2.3 How the ANOVA table is used 9.2.4 Estimation of group means 9.2.5 Comparison of group means 9.2.6 Fitted values 9.2.7 Using computer packages
9.3.1 Simple linear regression
9.4 Two categorical explanatory variables
9.3.2 Correlation 9.3.3 Nonlinear regression
9.4.1 Model specification
9.5 Model building 9.4.2 Model fitting 9.4.3 Balanced data 9.4.4 Unbalanced data 9.4.5 Fitted values 9.4.6 Least squares means 9.4.7 Interaction 9.6 General linear models 9.7 Several explanatory variables 9.8 Model checking 9.9 Confounding
9.9.1 Adjustment using residuals
9.10 Longitudinal data 9.11 Non-normal alternatives Exercises
10. Modelling binary outcome data
10.1 Introduction
10.2 Problems with standard regression models
10.2.1 The r-x relationship may well not be linear
10.3 Logistic regression 10.2.2 Predicted values of the risk may be outside the valid range 10.2.3 The error distribution is not normal 10.4 Interpretation of logistic regression coefficients
10.4.1 Binary risk factors
10.5 Generic data 10.4.2 Quantitative risk factors 10.4.3 Categorical risk factors 10.4.4 Ordinal risk factors 10.4.5 Floating absolute risks 10.6 Multiple logistic regression models 10.7 Tests of hypotheses
10.7.1 Goodness of fit for grouped data
10.8 Confounding 10.7.2 Goodness of fit for generic data 10.7.3 Effect of a risk factor 10.7.4 Tests for linearity and nonlinearity 10.7.5 Tests based upon estimates and their standard errors 10.7.6 Problems with missing values 10.9 Interaction
10.9.1 Between two categorical variables
10.10 Model checking
10.9.2 Between a quantitative and a categorical variable 10.9.3 Between two quantitative variables
10.10.1 Residuals
10.11 Regression dilution
10.10.2 Influential observations
10.11.1 Correcting for regression dilution
10.12 Case–control studies
10.12.1 Unmatched studies
10.13 Outcomes with several ordered levels
10.12.2 Matched studies
10.13.1 The proportional odds assumption
10.14 Longitudinal data 10.13.2 The proportional odds model 10.15 Complex sampling designs Exercises
11. Modelling follow-up data
11.1 Introduction
11.1.1 Models for survival data
11.2 Basic functions of survival time
11.2.1 The survival function
11.3 Estimating the hazard function
11.2.2 The hazard function
11.3.1 Kaplan–Meier estimation
11.4 Probability models
11.3.2 Person-time estimation 11.3.3 Actuarial estimation
11.4.1 The probability density and cumulative distribution functions
11.5 Proportional hazards regression models
11.4.2 Choosing a model 11.4.3 The exponential distribution 11.4.4 The Weibull distribution 11.4.5 Other probability models
11.5.1 Comparing two groups
11.6 The Cox proportional hazards model
11.5.2 Comparing several groups 11.5.3 Modelling with a quantitative variable 11.5.4 Modelling with several variables
11.6.1 Time-dependent covariates
11.7 The Weibull proportional hazards model 11.6.2 Recurrent events 11.8 Model checking
11.8.1 Log cumulative hazard plots
11.9 Poisson regression
11.8.2 An objective test of proportional hazards for the Cox model 11.8.3 An objective test of proportional hazards for the Weibull model 11.8.4 Residuals and influence 11.8.5 Nonproportional hazards
11.9.1 Simple regression
11.10 Pooled logistic regression 11.9.2 Multiple regression 11.9.3 Comparison of standardised event ratios 11.9.4 Routine or registration data 11.9.5 Generic data 11.9.6 Model checking Exercises
12. Meta-analysis
12.1 Reviewing evidence
12.1.1 The Cochrane Collaboration
12.2 Systematic review
12.2.1 Designing a systematic review
12.3 A general approach to pooling
12.2.2 Study quality
12.3.1 Inverse variance weighting
12.4 Investigating heterogeneity
12.3.2 Fixed effect and random effects 12.3.3 Quantifying heterogeneity 12.3.4 Estimating the between-study variance 12.3.5 Calculating inverse variance weights 12.3.6 Calculating standard errors from confidence intervals 12.3.7 Dealing with the normal approximation 12.3.8 Pooling risk differences 12.3.9 Pooling differences in mean values 12.3.10 Other quantities 12.3.11 Pooling mixed quantities
12.4.1 Forest plots
12.5 Pooling tabular data
12.4.2 Influence plots 12.4.3 Sensitivity analyses 12.4.4 Meta-regression
12.5.1 Inverse variance weighting
12.6 Individual participant data 12.5.2 Mantel–Haenszel methods 12.5.3 The Peto method 12.5.4 Dealing with zeros 12.5.5 Advantages and disadvantages of using tabular data 12.7 Dealing with aspects of study quality 12.8 Publication bias
12.8.1 The funnel plot
12.9 Is meta-analysis a valid tool in epidemiology? 12.8.2 Consequences of publication bias 12.8.3 Correcting for publication bias 12.8.4 Other causes of asymmetry in funnel plots Exercises
Appendix A. Materials available on the website for this book
A.1 SAS programs
A.2 Stata programs A.3 Sample size spreadsheet A.4 Floating absolute risk macros A.5 Data sets from the text
Appendix B. Statistical tables
Appendix C. Example data sets
Solutions to the exercises
References
Index
|
|||||||||||||||||||||
){kind=link}
){kind=link}