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Veterinary Epidemiologic Research
Comment from the Stata technical groupThis is a graduate-level text on the principles and methods of veterinary epidemiologic research. Although many of the examples relate to veterinary epidemiology, the principles apply equally to human epidemiology; only some of the diseases may not be familiar. All terms and epidemiological measures are clearly defined, and all notations and formulas are identified with examples. Designs discussed in this text include cohort studies, case–control studies, two-stage sampling designs, and controlled trials. Several statistical models are also discussed: linear regression, logistic regression, multinomial logistic regression, the Poisson model, survival analysis, and mixed-effects models. The datasets used in the examples, as well as examples using Stata, are thoroughly described in the text and are available on the book’s web site: www.upei.ca/ver. Table of contentsForeword
Preface
Reference list
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 Component-cause model 1.8 Causal-web model 1.9 Constructing a causal diagram 1.10 Causal criteria 2 Sampling
2.1 Introduction
2.2 Non-probability 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 Analysis of survey data 2.10 Sample-size determination 2.11 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 questionnaire 3.7 Pre-testing questionnaires 3.8 Data coding and editing 4 Measures of disease frequency
4.1 Introduction
4.2 Count, proportion, odds and rate 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 Confidence intervals 4.11 Standardisation of risks and rates 4.12 Application 5 Screening and diagnostic tests
5.1 Introduction
5.2 Laboratory-based concepts 5.3 The ability of a test to detect disease of health 5.4 Estimating test sensitivity and specificity 5.5 Predictive values 5.6 Using multiple tests 5.7 Estimating the true prevalence of disease 5.8 Sensitivity and specificity estimations using logistic regression 5.9 Estimating Se and Sp without a gold standard 5.10 Herd-level testing 6 Measures of association
6.1 Introduction
6.2 Measures of association 6.3 Measures of effect 6.4 Hypothesis testing and confidence intervals 7 Introduction to observational studies
7.1 Introduction
7.2 Descriptive studies 7.3 Observational analytic (explanatory) studies 7.4 Cross-sectional studies 7.5 Examples of cross-sectional studies 8 Cohort studies
8.1 Introduction
8.2 Basis 8.3 The exposure 8.4 Sample-size aspects 8.5 The nature of the exposure groups 8.6 Follow-up period and processes 8.7 Measuring the outcome 8.8 Analysis/interpretation 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 in risk-based designs 9.6 Selecting controls in rate-based designs 9.7 Other sources of controls 9.8 The issue of ‘representativeness’ 9.9 More than one control group 9.10 More than one control per case 9.11 Analysis of case-control data 10 Hybrid study designs
10.1 Introduction
10.2 Case-cohort studies
10.3 Case-crossover studies 10.4 Case-only studies 10.5 Two-stage sampling designs 11 Controlled trials
11.1 Introduction
11.2 Stating the objectives 11.3 The study population 11.4 Allocation of participants 11.5 Specifying the intervention 11.6 Masking (blinding) 11.7 Follow-up/compliance 11.8 Measuring the outcome 11.9 Analysis 11.10 Ethical considerations 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 Misclassification of multinomial exposure or disease categories 12.8 Validation studies to correct misclassification 12.9 Measurement error 12.10 Measurement error in surrogate measures of exposure 12.11 Misclassification and measurement errors — impact on sample size 13 Confounder bias: analytic control and matching
13.1 Introduction
13.2 Confounding and causation 13.3 What extraneous factors are confounders? 13.4 Criteria for confounding 13.5 Control of confounding 13.6 Matching 13.7 Analytic control of confounding 13.8 Stratified analysis to control confounding 13.9 Stratified analysis when interaction is present 13.10 External adjustment of odds ratios for unmeasured confounders 13.11 Multivariable causation and data analyses 13.12 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 X-variables 14.5 Modeling highly correlated (collinear) variables 14.6 Detecting and modeling 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 each observation 14.11 Comments on the model deficiencies 15 Model-building strategies
15.1 Introduction
15.2 Specifying the maximum model
15.3 Specify the selection criteria 15.4 Specifying the selection strategy 15.5 Conduct the analysis 15.6 Evaluate the reliability of the mode l 15.7 Presenting the results 16 Logistic regression
16.1 Introduction
16.2 The logit transform 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 Model building 16.11 Evaluating logistic regression models 16.12 Sample size considerations 16.13 Logistic regression using data from complex sample surveys 16.14 Conditional logistic regression for matched studies 17 Modelling multinomial data
17.1 Introduction
17.2 Overview of models 17.3 Multinomial logistic regression 17.4 Modelling ordinal data 17.5 Adjacent-category model 17.6 Continuation-ratio model 17.7 Proportional-odds model (constrained cumulative logit 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 Zero-inflated models 19 Modelling survival data
19.1 Introduction
19.2 Non-parametric analysis 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 non-parametric analyses 19.7 Survivor, failure and hazard functions 19.8 Semi-parametric analyses 19.9 Parametric models 19.10 Accelerated failure time models 19.11 Multiple outcome event data 19.12 Frailty models 19.13 Sample-size considerations 20 Introduction to clustered data
20.1 Introduction
20.2 Clustering arising from the data structure 20.3 Effects of clustering 20.4 Introduction to methods of dealing with clustering 21 Mixed models for continuous data
21.1 Introduction
21.2 Linear mixed model 21.3 Random slopes 21.4 Statistical analysis of linear mixed models 21.5 Repeated measures and spatial data 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 Repeated measures and spatial data 23 Alternative approaches to dealing with clustered data
23.1 Introduction
23.2 Simpler methods 23.3 Generalised estimating equations 23.4 Bayesian analysis 23.5 Summary of clustered data analysis 24 Meta-analysis
24.1 Introduction
24.2 Objectives of meta-analysis 24.3 Meta-analysis process 24.4 Analytical procedures in meta-analyses 24.5 Use of meta-analysis 25 Ecologic and group-level studies
25.1 Introduction
25.2 Rationale for studying groups 25.3 Types of ecologic variable 25.4 Issues related to modelling approaches in group level studies 25.5 Issues related to inferences 25.6 Sources of ecologic bias 25.7 Non-ecologic group-level studies 26 A structured approach to data analysis
26.1 Introduction
26.2 Data collection sheets 26.3 Data coding 26.4 Data entry 26.5 Keeping track of files 26.6 Keeping track of variables 26.7 Program mode vs interactive processing 26.8 Data editing 26.9 Data verification 26.10 Data processing — outcome variable(s) 26.11 Data processing — predictor variables 26.12 Data processing — multilevel data 26.13 Unconditional associations 26.14 Keeping track of your analyses 27 Description of datasets
28 Program files
Glossary and terminology
GT.1 Data layout
GT.2 Multivariable models GT.3 Glossary GT.4 Probability notation GT.5 Naming variables Combined reference list
Index
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