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Methods in Epidemiologic Research 

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Comment from the Stata technical groupIn Methods in Epidemioloic Research, Ian Dohoo, Wayne Martin, and Henrik Stryhn have created a comprehensive handbook about all facets of the subject. They converted their popular book about veterinary research to one about human popuations, with the corresponding gain that crosspollination of specialties brings. The book covers a wide breadth of topics—from design of studies through statistical analysis to effective communication of results. It emphasizes the need to truly show causality rather than association, explaining tools for this in both, the design and the analysis stages of a study. It covers standard statistical methods, both basic and advanced, including introductions to spatial and Bayesian analysis methods. The book is not biased to any statistical software, but all the datasets and dofiles used for the computations in the examples are available from upei.ca/mer/datasetsprograms. Those with a statistical background can use it to learn to speak the language of epidemiologists; those with an epidemiologic background can use it to learn to speak the language of statisticians. Each chapter has an extensive and uptodate bibliography, which allows you to use the book as indepth selfteaching tool. 

Table of contentsView table of contents >> Forewords
Preface
Acknowledgements
1 INTRODUCTION AND CAUSAL CONCEPTS
1.1 Introduction
1.2 A brief history of multiple causation concepts 1.3 A brief history of scientific inference 1.4 Key components of epidemiologic research 1.5 Seeking causes 1.6 Models of causation 1.7 Counterfactual concepts of causation for a single exposure 1.8 Experimental versus observational evidence of causation 1.9 Constructing a causal diagram 1.10 Causal criteria 2 SAMPLING
2.1 Introduction
2.2 Nonprobability sampling 2.3 Probability sampling 2.4 Simple random sample 2.5 Systematic random sample 2.6 Stratified random sample 2.7 Cluster sampling 2.8 Multistage sampling 2.9 Targeted (riskbased) sampling 2.10 Analysis of survey data 2.11 Samplesize determination 2.12 Sampling to detect disease 3 QUESTIONNAIRE DESIGN
3.1 Introduction
3.2 Designing the question 3.3 Open question 3.4 Closed question 3.5 Wording the question 3.6 Structure of questionnaires 3.7 Pretesting questionnaires 3.8 Validation 3.9 Response rate 3.10 Datacoding and editing 4 MEASURES OF DISEASE FREQUENCY
4.1 Introduction
4.2 Counts, proportions, odds, and rates 4.3 Incidence 4.4 Calculating risk 4.5 Calculating incidence rates 4.6 Relationship between risk and rate 4.7 Prevalence 4.8 Mortality statistics 4.9 Other measures of disease frequency 4.10 Standard errors and confidence intervals 4.11 Standardisation of risks and rates 5 SCREENING AND DIAGNOSTIC TESTS
5.1 Introduction
5.2 Attributes of the test per se 5.3 The ability of a test to detect disease or health 5.4 Predictive values 5.5 Interpreting test results that are measured on a continuous scale 5.6 Using multiple tests 5.7 Evaluation of diagnostic tests 5.8 Evaluation when there is no gold standard 5.9 Other considerations in test evaluation 5.10 Sample size requirements 5.11 Grouplevel testing 5.12 Use of pooled samples 6 MEASURES OF ASSOCIATION
6.1 Introduction
6.2 Measures of association 6.3 Measures of effect 6.4 Study design and measures of association 6.5 Hypothesis testing and confidence intervals 6.6 Multivariable estimation of measures of association 7 INTRODUCTION TO OBSERVATIONAL STUDIES
7.1 Introduction
7.2 A unified approach to study design 7.3 Descriptive studies 7.4 Observational studies 7.5 Crosssectional studies 7.6 Estimating incidence from one or more crosssectional studies 7.7 Inferential limitations of crosssectional studies 7.8 Repeated crosssectional versus cohort studies 7.9 Reporting of observational studies 8 COHORT STUDIES
8.1 Introduction
8.2 Selecting the study group 8.3 The exposure 8.4 Disease as exposure 8.5 Ensuring exposed and nonexposed groups are comparable 8.6 Followup period 8.7 Measuring the outcome 8.8 Analysis 8.9 Reporting of cohort studies 9 CASE–CONTROL STUDIES
9.1 Introduction
9.2 The study base 9.3 The case series 9.4 Principles of control selection 9.5 Selecting controls and data layout in riskbased designs 9.6 Sampling controls and data layout in ratebased designs 9.7 Other sources of controls 9.8 The number of controls per case 9.9 The number of control groups 9.10 Exposure and covariate assessment 9.11 Keeping the cases and controls comparable 9.12 Analysis of case–control data 9.13 Reporting guidelines for case–control data 10 HYBRID STUDY DESIGNS
10.1 Introduction
10.2 Casecrossover studies 10.3 Case–case studies 10.4 Casecase–control studies 10.5 Case–series studies 10.6 Case–cohort studies 10.7 Caseonly studies 10.8 Twostage sampling designs 11 CONTROLLED STUDIES
11.1 Introduction
11.2 Background, objectives, and summary trial design 11.3 Participants: the study group 11.4 Specifying the intervention 11.5 Measuring the outcome 11.6 Sample size 11.7 Allocation of study subjects 11.8 Followup/compliance 11.9 Statistical methods and analysis 11.10 Conclusions 11.11 Clinical trial designs for prophylaxis of communicable organisms 11.12 Reporting of clinical trials 12 VALIDITY IN OBSERVATIONAL STUDIES
12.1 Introduction
12.2 Selection bias 12.3 Examples of selection bias 12.4 Reducing selection bias 12.5 Information bias 12.6 Bias from misclassification 12.7 Validation studies to correct misclassification 12.8 Measurement error 12.9 Errors in surrogate measures of exposure 12.10 The impact of information bias on sample size 13 CONFOUNDING: DETECTION AND CONTROL
13.1 Introduction
13.2 Control of confounding prior to data analysis 13.3 Matching on confounders 13.4 Detection of confounding 13.5 Analytic control of confounding 13.6 Multivariable modelling to control confounding 13.7 Other approaches to control confounding and estimate causal effects 13.8 Propensity scores for controlling confounding 13.9 External adjustment and sensitivity analysis for unmeasured confounders 13.10 Understanding causal relationships 13.11 Summary of effects of extraneous variables 14 LINEAR REGRESSION
14.1 Introduction
14.2 Regression analysis 14.3 Hypothesis testing and effect estimation 14.4 Nature of the Xvariables 14.5 Detecting highly correlated (collinear) variables 14.6 Detecting and modelling interaction 14.7 Causal interpretation of a multivariable linear model 14.8 Evaluating the least squares model 14.9 Evaluating the major assumptions 14.10 Assessment of individual observations 14.11 Timeseries data 15 MODELBUILDING STRATEGIES
15.1 Introduction
15.2 Steps in building a model 15.3 Building a causal model 15.4 Reducing the number of predictors 15.5 The problem of missing values 15.6 Effects of continuous predictors 15.7 Identifying interaction terms of interest 15.8 Building the model 15.9 Evaluate the reliability of the model 15.10 Presenting the results 16 LOGISTIC REGRESSION
16.1 Introduction
16.2 The logistic model 16.3 Odds and odds ratios 16.4 Fitting a logistic regression model 16.5 Assumptions in logistic regression 16.6 Likelihood ratio statistics 16.7 Wald tests 16.8 Interpretation of coefficients 16.9 Assessing interaction and confounding 16.10 Modelbuilding 16.11 Generalised linear models 16.12 Evaluating logistic regression models 16.13 Sample size considerations 16.14 Exact logistic regression 16.15 Conditional logistic regression for matched studies 17 MODELLING ORDINAL AND MULTINOMIAL DATA
17.1 Introduction
17.2 Overview of models 17.3 Multinomial logistic regression 17.4 Modelling ordinal data 17.5 Proportional odds model (constrained cumulative logit model) 17.6 Adjacentcategory model 17.7 Continuationratio model 18 MODELLING COUNT AND RATE DATA
18.1 Introduction
18.2 The Poisson distribution 18.3 Poisson regression model 18.4 Interpretation of coefficients 18.5 Evaluating Poisson regression models 18.6 Negative binomial regression 18.7 Problems with zero counts 19 MODELLING SURVIVAL DATA
19.1 Introduction
19.2 Nonparametric analyses 19.3 Actuarial life tables 19.4 Kaplan–Meier estimate of survivor function 19.5 Nelson–Aalen estimate of cumulative hazard 19.6 Statistical inference in nonparametric analyses 19.7 Survivor, failure, and hazard functions 19.8 Semiparametric analyses 19.9 Parametric models 19.10 Accelerated failure time models 19.11 Frailty models and clustering 19.12 Multiple outcome event data 19.13 Discretetime survival analysis 19.14 Samples sizes for survival analyses 20 INTRODUCTION TO CLUSTERED DATA
20.1 Introduction
20.2 Clustering arising from the data structure 20.3 Effects of clustering 20.4 Simulation studies on the impact of clustering 20.5 Introduction to methods for dealing with clustering 21 MIXED MODELS FOR CONTINUOUS DATA
21.1 Introduction
21.2 Linear mixed model 21.3 Random slopes 21.4 Contextual effects 21.5 Statistical analysis of linear mixed models 22 MIXED MODELS FOR DISCRETE DATA
22.1 Introduction
22.2 Logistic regression with random effects 22.3 Poisson regression with random effects 22.4 Generalised linear mixed model 22.5 Statistical analysis of GLMMs 22.6 Summary remarks on analysis of discrete clustered data 23 REPEATED MEASURES DATA
23.1 Introduction to repeated measures data
23.2 Univariate and multivariate approaches to repeated measures data 23.3 Linear mixed models with correlation structure 23.4 Mixed models for discrete repeated measures data 23.5 Generalised estimating equations 24 INTRODUCTION TO BAYESIAN ANALYSIS
24.1 Introduction
24.2 Bayesian analysis 24.3 Markov chain Monte Carlo estimation 24.4 Statistical analysis based on MCMC estimation 24.5 Extensions of Bayesian and MCMC modelling 25 ANALYSIS OF SPATIAL DATA: INTRODUCTION AND VISUALISATION
25.1 Introduction
25.2 Spatial data 25.3 Spatial data analysis 25.4 Additional topics 26 ANALYSIS OF SPATIAL DATA
26.1 Introduction
26.2 Issues specific to statistical analysis of spatial data 26.3 Exploratory spatial analysis 26.4 Global spatial clustering 26.5 Localised spatial cluster detection 26.6 Spacetime association 26.7 Modelling 27 CONCEPTS OF INFECTIOUS DISEASE EPIDEMIOLOGY
27.1 Introduction
27.2 Infection vs disease 27.3 Transmission 27.4 Mathematical modelling of infectious disease transmission 27.5 Methods of control of infectious disease 27.6 Estimating R_{0} and other parameters 27.7 Developing more complex models 27.8 Using models 27.9 Summary 28 SYSTEMATIC REVIEWS AND METAANALYSIS
28.1 Introduction
28.2 Narrative reviews 28.3 Systematic reviews 28.4 Metaanalysis—introduction 28.5 Fixed and randomeffects models 28.6 Presentation of results 28.7 Heterogeneity 28.8 Publication bias 28.9 Influential studies 28.10 Outcome scales and data issues 28.11 Metaanalysis of observational studies 28.12 Metaanalysis of diagnostic tests 28.13 Use of metaanalysis 29 ECOLOGICAL AND GROUPLEVEL STUDIES
29.1 Introduction
29.2 Rationale for group level studies 29.3 Types of ecologic variable 29.4 Issues related to modelling approaches in ecologic studies 29.5 The linear model in the context of ecologic studies 29.6 Issues related to inferences 29.7 Sources of ecologic bias 29.8 Analysis of ecologic data 29.9 Nonecologic grouplevel studies 30 A STRUCTURED APPROACH TO DATA ANALYSIS
30.1 Introduction
30.2 Datacollection sheets 30.3 Data coding 30.4 Data entry 30.5 Keeping track of files 30.6 Keeping track of variables 30.7 Program mode versus interactive processing 30.8 Dataediting 30.9 Data verification 30.10 Data processing—outcome variables 30.11 Data processing—predictor variables 30.12 Data processing—multilevel data 30.13 Unconditional associations 30.14 Keeping track of your analyses 31 DESCRIPTION OF DATASETS

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