Table of Contents

Veterinary Epidemiologic Research is a comprehensive text covering the key principles and methods used in veterinary epidemiologic research. It is written primarily for researchers and graduate students in veterinary epidemiology, but the material is equally applicable to those in related disciplines (human epidemiology, public health etc).

The first 13 chapters are devoted to issues related to the design and execution of observational studies and controlled trials.

Chapters 14 through 24 cover the statistical (multivariable) methods commonly used in the analysis of epidemiologic studies, including extensive coverage of mixed (random effects) models.

The book concludes with chapters dealing with spatial data, infectious disease epidemiology, meta-analysis, and ecologic studies. Extensive use is made of worked examples to demonstrate the principles being covered. All datasets referred to in the book are described in the book (Chapter 31) and on this website. Listings of program files (primarily Stata -do- files) used in all examples are provided on this website.

1. INTRODUCTION AND CAUSAL CONCEPTS
1.1 Introduction 2
1.2 A brief history of multiple causation concepts 2
1.3 A brief history of scientific inference 5
1.4 Key components of epidemiologic research 8
1.5 Seeking causes 9
1.6 Models of causation 10
1.7 Counterfactual concepts of causation for a single exposure 17
1.8 Experimental versus observational evidence of causation 20
1.9 Constructing a causal diagram 21
1.10 Causal criteria 23
2. SAMPLING
2.1 Introduction 34
2.2 Non-probability sampling 34
2.3 Probability sampling 37
2.4 Simple random sample 37
2.5 Systematic random sample 38
2.6 Stratified random sample 38
2.7 Cluster sampling 38
2.8 Multistage sampling 40
2.9 Targeted (risk-based) sampling 41
2.10 Analysis of survey data 42
2.11 Sample-size determination 46
2.12 Sampling to detect disease 53
3. QUESTIONNAIRE DESIGN
3.1 Introduction 58
3.2 Designing the question 60
3.3 Open question 61
3.4 Closed question 61
3.5 Wording the question 64
3.6 Structure of questionnaires 65
3.7 Pre-testing questionnaires 66
3.8 Validation 67
3.9 Response Rate 67
3.10 Data-coding and editing 68
4. MEASURES OF DISEASE FREQUENCY
4.1 Introduction 74
4.2 Count, proportion, odds and rate 74
4.3 Incidence 75
4.4 Calculating risk 76
4.5 Calculating incidence rates 77
4.6 Relationship between risk and rate 79
4.7 Prevalence 80
4.8 Mortality statistics 81
4.9 Other measures of disease frequency 81
4.10 Standard errors and confidence intervals 83
4.11 Standardisation of risks and rates 85
5. SCREENING AND DIAGNOSTIC TESTS
5.1 Introduction 92
5.2 Attributes of the test per se 92
5.3 The ability of a test to detect disease or health 100
5.4 Predictive values 103
5.5 Interpreting test results that are measured on a continuous scale 105
5.6 Using multiple tests 111
5.7 Evaluation of diagnostic tests 114
5.8 Evaluation when there is no gold standard 116
5.9 Other considerations in test evaluation 122
5.10 Sample size requirements 123
5.11 Herd-level testing 123
5.12 Use of pooled samples 127
6. MEASURES OF ASSOCIATION
6.1 Introduction 136
6.2 Measures of association 137
6.3 Measures of effect 139
6.4 Study design and measures of association 143
6.5 Hypothesis testing and confidence intervals 143
6.6 Multivariable estimation of measures of association 148
7. INTRODUCTION TO OBSERVATIONAL STUDIES
7.1 Introduction 152
7.2 A unified approach to study design 154
7.3 Descriptive studies 156
7.4 Observational studies 157
7.5 Cross-sectional studies 158
7.6 Repeated cross-sectional versus cohort studies 162
8. COHORT STUDIES
8.1 Introduction 168
8.2 Study group 169
8.3 The exposure 171
8.4 Ensuring exposed and non-exposed groups are comparable 174
8.5 Follow-up period 175
8.6 Measuring the outcome 175
8.7 Analysis 176
8.8 Reporting of cohort studies 177
9. CASE-CONTROL STUDIES
9.1 Introduction 182
9.2 The study base 182
9.3 The case series 183
9.4 Principles of control selection 184
9.5 Selecting controls in risk-based designs 185
9.6 Selecting controls in rate-based designs 187
9.7 Other sources of controls 190
9.8 The number of controls per case 193
9.9 The number of control groups 193
9.10 Exposure and covariate assessment 193
9.11 Keeping the cases and controls comparable 193
9.12 Analysis of case-control data 194
9.13 Reporting guidelines for case-control studies 195
10. HYBRID STUDY DESIGNS
10.1 Introduction 200
10.2 Case-crossover studies 200
10.3 Case-case studies 203
10.4 Case-series studies 204
10.5 Case-cohort studies 206
10.6 Case-only studies 208
10.7 Two-stage sampling designs 209
11. CONTROLLED STUDIES
11.1 Introduction 214
11.2 Stating the objectives 215
11.3 The study group 216
11.4 Allocation of study subjects 221
11.5 Specifying the intervention 225
11.6 Masking (blinding) 225
11.7 Follow-up/compliance 226
11.8 Measuring the outcome 227
11.9 Analysis 227
11.10 Clinical trial designs for prophylaxis of communicable organisms 230
11.11 Ethical considerations 233
11.12 Reporting of clinical trials 235
12. VALIDITY IN OBSERVATIONAL STUDIES
12.1 Introduction 244
11.2 Examples of selection bias 244
12.3 Examples of selection bias 249
12.4 Reducing selection bias 254
12.5 Information bias 255
12.6 Bias from misclassification 257
12.7 Validation studies to correct misclassification 263
12.8 Measurement error 264
12.9 Errors in surrogate measures of exposure 265
12.10 The impact of information bias on sample size 266
13. CONFOUNDING: DETECTION AND CONTROL
13.1 Introduction 272
13.2 Control of confounding prior to data analysis 275
13.3 Matching on confounders 276
13.4 Matching using propensity scores 281
13.5 Detection of confounding 283
13.6 Analytic Control of Confounding 288
13.7 Other approaches to control confounding and estimate causal effects 295
13.8 Multivariable modelling to control confounding 301
13.9 Instrumental variables to control confounding 302
13.10 External adjustment and sensitivity analysis for unmeasured confounders 304
13.11 Understanding causal relationships 306
13.12 Summary of effects of extraneous variables 315
14. LINEAR REGRESSION
14.1 Introduction 324
14.2 Regression analysis 324
14.3 Hypothesis testing and effect estimation 326
14.4 Nature of the X-variables 333
14.5 Detecting highly correlated (collinear) variables 338
14.6 Detecting and modelling interaction 340
14.7 Causal interpretation of a multivariable linear model 341
14.8 Evaluating the least squares model 344
14.9 Evaluating the major assumptions 349
14.10 Assessment of individual observations 356
14.11 Time-series data 360
15. MODEL-BUILDING STRATEGIES
15.1 Introduction 366
15.2 Steps in building a model 367
15.3 Building a causal model 367
15.4 Reducing the number of predictors 368
15.5 The problem of missing values 374
15.6 Effects of continuous predictors 375
15.7 Identifying interaction terms of interest 381
15.8 Building the model 383
15.9 Evaluate the reliability of the model 388
15.10 Presenting the results 390
16. LOGISTIC REGRESSION
16.1 Introduction 396
16.2 The logistic model 396
16.3 Odds and odds ratios 397
16.4 Fitting a logistic regression model 398
16.5 Assumptions in logistic regression 399
16.6 Likelihood ratio statistics 400
16.7 Wald tests 401
16.8 Interpretation of coefficients 402
16.9 Assessing interaction and confounding 405
16.10 Model-building 408
16.11 Generalised linear models 408
16.12 Evaluating logistic regression models 410
16.13 Sample size considerations 421
16.14 Exact logistic regression 421
16.15 Conditional logistic regression for matched studies 422
17. MODELLING ORDINAL AND MULTINOMIAL DATA
17.1 Introduction 428
17.2 Overview of models 429
17.3 Multinomial logistic regression 431
17.4 Modelling ordinal data 436
17.5 Proportional odds model (constrained cumulative logit model) 437
17.6 Adjacent-category model 441
17.7 Continuation-ratio model 443
18. MODELLING COUNT AND RATE DATA
18.1 Introduction 446
18.2 The Poisson distribution 447
18.3 Poisson regression model 448
18.4 Interpretation of coefficients 449
18.5 Evaluating Poisson regression models 451
18.6 Negative binomial regression 454
18.7 Problems with zero counts 461
19. MODELLING SURVIVAL DATA
19.1 Introduction 468
19.2 Non-parametric analyses 473
19.3 Actuarial life tables 473
19.4 Kaplan-Meier estimate of survivor function 475
19.5 Nelson-Aalen estimate of cumulative hazard 478
19.6 Statistical inference in non-parametric analyses 479
19.7 Survivor, failure and hazard functions 480
19.8 Semi-parametric analyses 485
19.9 Parametric models 503
19.10 Accelerated failure time models 507
19.11 Frailty models and clustering 510
19.12 Multiple outcome event data 517
19.13 Discrete-time survival analysis 518
19.14 Sample sizes for survival analyses 522
20. INTRODUCTION TO CLUSTERED DATA
20.1 Introduction 530
20.2 Clustering arising from the data structure 530
20.3 Effects of clustering 536
20.4 Simulation studies on the impact of clustering 540
20.5 Introduction to methods for dealing with clustering 542
21. MIXED MODELS FOR CONTINUOUS DATA
21.1 Introduction 554
21.2 Linear mixed model 555
21.3 Random slopes 560
21.4 Contextual effects 564
21.5 Statistical analysis of linear mixed models 565
22. MIXED MODELS FOR DISCRETE DATA
22.1 Introduction 580
22.2 Logistic regression with random effects 580
22.3 Poisson regression with random effects 584
22.4 Generalised linear mixed model 587
22.5 Statistical analysis of GLMMs 593
22.6 Summary remarks on analysis of discrete clustered data 603
23. REPEATED MEASURES DATA
23.1 Introduction to repeated measures data 608
23.2 Univariate and multivariate approaches to repeated measures data 611
23.3 Linear mixed models with correlation structure 616
23.4 Mixed models for discrete repeated measures data 624
23.5 Generalised estimating equations 627
24. INTRODUCTION TO BAYESIAN ANALYSIS
24.1 Introduction 638
24.2 Bayesian analysis 638
24.3 Markov chain Monte Carlo (MCMC) estimation 642
24.4 Statistical analysis based on MCMC estimation 647
24.5 Extensions of Bayesian and MCMC Modelling 651
25. ANALYSIS OF SPATIAL DATA: INTRODUCTION AND VISUALISATION
25.1 Introduction 664
25.2 Spatial data 664
25.3 Spatial data analysis 667
25.4 Additional topics 673
26. ANALYSIS OF SPATIAL DATA
26.1 Introduction 680
26.2 Issues specific to statistical analysis of spatial data 680
26.3 Exploratory spatial analysis 682
26.4 Global spatial clustering 690
26.5 Localised spatial cluster detection 697
26.6 Space-time association 700
26.7 Modelling 704
27. CONCEPTS OF INFECTIOUS DISEASE EPIDEMIOLOGY
27.1 Introduction 716
27.2 Infection vs disease 718
27.3 Transmission 719
27.4 Mathematical modelling of infectious disease transmission 721
27.5 Estimating R0 and other infectious disease parameters 725
28. SYSTEMATIC REVIEWS AND META-ANALYSIS
28.1 Introduction 740
28.2 Narrative reviews 740
28.3 Systematic Reviews 741
28.4 Meta-analysis – Introduction 745
28.5 Fixed- and random-effects models 746
28.6 Presentation of results 749
28.7 Heterogeneity 750
28.8 Publication bias 758
28.9 Influential studies 760
28.10 Outcome scales and data issues 760
28.11 Meta-analysis of observational studies 764
28.12 Meta-analysis of diagnostic tests 766
28.13 Use of meta-analysis 766
29. ECOLOGICAL AND GROUP-LEVEL STUDIES
29.1 Introduction 774
29.2 Rationale for group level studies 774
29.3 Types of ecologic variable 775
29.4 Issues related to modelling approaches in ecologic studies 776
29.5 Issues related to inferences 778
29.6 Sources of ecologic bias 778
29.7 Non-ecologic group-level studies 782
30. A STRUCTURED APPROACH TO DATA ANALYSIS
30.1 Introduction 790
30.2 Data-collection sheets 790
30.3 Data coding 791
30.4 Data entry 791
30.5 Keeping track of files 792
30.6 Keeping track of variables 792
30.7 Program mode versus interactive processing 793
30.8 Data-editing 794
30.9 Data verification 795
30.10 Data processing—outcome variable(s) 795
30.11 Data processing—predictor variables 796
30.12 Data processing—multilevel data 796
30.13 Unconditional associations 796
30.14 Keeping track of your analyses 797
 31. DESCRIPTION OF DATASETS 799