Epidemiology: study design and data analysis / Mark Woodward

Incluye bibliografía: páginas 799-819 e índice: páginas 821-832

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.. 1.3.2 Samples.. 1.4 Measuring disease.. 1.4.1 Incidence and prevalence.. 1.5 Measuring the risk factor.. 1.6 Causality.. 1.6.1 Association.. 1.6.2 Problems with establishing causality.. 1.6.3 Principles of causality.. 1.7 Studies using routine data.. 1.7.1 Ecological data.. 1.7.2 National sources of data on disease.. 1.7.3 National sources of data on risk factors.. 1.7.4 International data.. 1.8 Study design.. 1.8.1 Intervention studies.. 1.8.2 Observational studies.. 1.9 Data analysis.. 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.. 2.3.2 Quantitative variables.. 2.3.3 The hierarchy of type.. 2.4 Tables and charts.. 2.4.1 Tables in reports.. 2.4.2 Diagrams in reports.. 2.5 Inferential techniques for categorical variables.. 2.5.1 Contingency tables.. 2.5.2 Binary variables: proportions and percentages.. 2.5.3 Comparing two proportions or percentages.. 2.6 Descriptive Techniques for quantitative variables.. 2.6.1 The five-number summary.. 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 Inferences about means.. 2.7.1 Checking normality.. 2.7.2 Inferences for a single mean.. 2.7.3 Comparing two means.. 2.7.4 Paired data.. 2.8 Inferential techniques for non-normal data.. 2.8.1 Transformations.. 2.8.2 Nonparametric tests.. 2.8.3 Confidence intervals for medians.. 2.9 Measuring agreement.. 2.9.1 Quantitative variables.. 2.9.2 Categorical variables.. 2.9.3 Ordered categorical variables.. 2.9.4 Internal consistency.. 2.10 Assessing diagnostic tests.. 2.10.1 Accounting for sensitivity and specificity.. Exercises

3 Assessing risk factors.. 3.1 Risk and relative risk.. 3.2 Odds and odds ratio.. 3.3 Relative risk or odds ratio?.. 3.4 Prevalence studies.. 3.5 Testing association.. 3.5.1 Equivalent tests.. 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 Risk factors measured at several levels.. 3.6.1 Continuous risk factors.. 3.6.2 A test for linear trend.. 3.6.3 A test for nonlinearity.. 3.7 Attributable risk.. 3.8 Rate and relative rate.. 3.8.1 The general epidemiological rate.. 3.9 Measures of difference.. 3.10 EPITAB commands in Stata.. 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.. 4.4.2 Using hypothesis tests.. 4.4.3 Dealing with several confounding variables.. 4.5 Standardisation.. 4.5.1 Direct standardisation of event rates.. 4.5.2 Indirect standardisation of event rates.. 4.5.3 Standardisation of risks.. 4.6 Mantel-Haenszel methods.. 4.6.1 The Mantel-Haenszel relative risk.. 4.6.2 The Cochran-Mantel-Haenszel test.. 4.6.3 Further comments.. 4.7 The concept of interaction.. 4.8 Testing for interaction.. 4.8.1 Using the relative risk.. 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?.. 4.9 Dealing with interaction.. 4.10 EPITAB commands in Stata.. Exercises.. 5 Cohort studies.. 5.1 Design considerations.. 5.1.1 Advantages.. 5.1.2 Disadvantages.. 5.1.3 Alternative designs with economic advantages.. 5.1.4 Studies with a single baseline sample.. 5.2 Analytical considerations.. 5.2.1 Concurrent follow-up.. 5.2.2 Moving baseline dates.. 5.2.3 Varying follow-up durations.. 5.2.4 Withdrawals.. 5.3 Cohort life tables.. 5.3.1 Allowing for sampling variation.. 5.3.2 Allowing for censoring

5.3.3 Comparison of two life tables.. 5.3.4 Limitations.. 5.4 Kaplan-Meier estimation.. 5.4.1 An empirical comparison.. 5.5 Comparison of two sets of survival probabilities.. 5.5.1 Mantel-Haenszel methods.. 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 of more groups.. 5.6 Competing risk.. 5.7 The person-years method.. 5.7.1 Age-specific rates.. 5.7.2 Summarisation of rates.. 5.7.3 Comparison of two SERs.. 5.7.4 Mantel-Haenszel methods.. 5.7.5 Further comments.. 5.8 Period-cohort analysis.. 5.8.1 Period-specific rates.. Exercises.. 6 Case-control studies.. 6.1 Basic design concepts.. 6.1.1 Advantages.. 6.1.2 Disadvantages.. 6.2 Basic methods of analysis.. 6.2.1 Dichotomous exposure.. 6.2.2 Polytomous exposure.. 6.2.3 Confounding and interaction.. 6.2.4 Attributable risk.. 6.3 Selection of cases.. 6.3.1 Definition.. 6.3.2 Inclusion and exclusion criteria.. 6.3.3 Incident or prevalent?.. 6.3.4 Source.. 6.3.5 Consideration of bias.. 6.4 Selection of controls.. 6.4.1 General principles.. 6.4.2 Hospital controls.. 6.4.3 Community controls.. 6.4.4 Other sources.. 6.4.5 How many?.. 6.5 Matching.. 6.5.1 Advantages.. 6.5.2 Disadvantages.. 6.5.3 One-to-many matching.. 6.5.4 Matching in other study designs.. 6.6 The analysis of matched studies.. 6.6.1 1 : 1 Matching.. 6.6.2 1 : c Matching.. 6.6.3 1 : Variable matching.. 6.6.4 Many : many matching.. 6.6.5 A modelling approach.. 6.7 Nested case-control studies.. 6.7.1 Matched studies.. 6.7.2 Counter-matched studies.. 6.8 Case-cohort studies.. 6.9 Case-crossover studies.. Exercises.. 7 Intervention studies.. 7.1 Introduction.. 7.1.1 Advantages.. 7.1.2 Disadvantages.. 7.2 Ethical considerations.. 7.2.1 The protocol.. 7.3 Avoidance of bias.. 7.3.1 Use of a control group.. 7.3.2 Blindness.. 7.3.3 Randomisation.. 7.3.4 Consent before randomisation.. 7.3.5 Analysis by intention-to-treat

7.4 Parallel group studies.. 7.4.1 Number needed to treat.. 7.4.2 Cluster randomised trials.. 7.4.3 Stepped wedge trials.. 7.4.4 Non-inferiority trials.. 7.5 Cross-over studies.. 7.5.1 Graphical analysis.. 7.5.2 Comparing means.. 7.5.3 Analysing preferences.. 7.5.4 Analysing binary data.. 7.6 Sequential studies.. 7.6.1 The Haybittle-Peto stopping rule.. 7.6.2 Adaptive designs.. 7.7 Allocation to treatment group.. 7.7.1 Global randomisation.. 7.7.2 Stratified randomization.. 7.7.3 Implementation.. 7.8 Trials as cohorts.. Exercises.. 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.. 8.3.2 Using a table of sample sizes.. 8.3.3 The minimum detectable difference.. 8.3.4 The assumption of known standard deviation.. 8.4 Testing a difference between means.. 8.4.1 Using a table of sample sizes.. 8.4.2 Power and minimum detectable difference.. 8.4.3 Optimum distribution of the sample.. 8.4.4 Paired data.. 8.5 Testing a proportion.. 8.5.1 Using a table of sample sizes.. 8.6 Testing a relative risk.. 8.6.1 Using a table of sample sizes.. 8.6.2 Power and minimum detectable relative risk.. 8.7 Case-control studies.. 8.7.1 Using a table of sample sizes.. 8.7.2 Power and minimum detectable relative risk.. 8.7.3 Comparison with cohort studies.. 8.7.4 Matched studies.. 8.8 Complex sampling designs.. 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.. 9.2.2 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 One quantitative explanatory variable.. 9.3.1 Simple linear regression.. 9.3.2 Correlation.. 9.3.3 Nonlinear regression

9.4 Two categorical explanatory variables.. 9.4.1 Model specification.. 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.. 9.5 Model building.. 9.6 General linear models.. 9.7 Several explanatory variables.. 9.7.1 Information criteria.. 9.7.2 Boosted regression.. 9.8 Model checking.. 9.9 Confounding.. 9.9.1 Adjustment using residuals.. 9.10 Splines.. 9.10.1 Choice of knots.. 9.10.2 Other types of splines.. 9.11 Panel data.. 9.12 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.. 10.2.2 Predicted values of the risk may be outside the valid range.. 10.2.3 The error distribution is not normal.. 10.3 Logistic regression.. 10.4 Interpretation of logistic regression coefficients.. 10.4.1 Binary risk factors.. 10.4.2 Quantitative risk factors.. 10.4.3 Categorical risk factors.. 10.4.4 Ordinal risk factors.. 10.4.5 Floating absolute risks.. 10.5 Generic data.. 10.6 Multiple logistic regression models.. 10.7 Tests of hypotheses.. 10.7.1 Goodness of fit for grouped data.. 10.7.2 Goodness of fit for generic data.. 10.7.3 Effect of a risk factor.. 10.7.4 Information criteria.. 10.7.5 Tests for linearity and nonlinearity.. 10.7.6 Tests based upon estimates and their standard errors.. 10.7.7 Problems with missing values.. 10.8 Confounding.. 10.9 Interaction.. 10.9.1 Between two categorical variables.. 10.9.2 Between a quantitative and categorical variable.. 10.9.3 Between two quantitative variables.. 10.10 Dealing with a quantitative explanatory variable.. 10.10.1 Linear form.. 10.10.2 Categorical form.. 10.10.3 Linear spline form.. 10.10.4 Generalisations.. 10.11 Model checking.. 10.11.1 Residuals.. 10.11.2 Influential observations.. 10.12 Measurement error

10.12.1 Regression to the mean.. 10.12.2 Correcting for regression dilution.. 10.13 Case-control studies.. 10.13.1 Unmatched studies.. 10.13.2 Matched studies.. 10.14 Outcomes with several levels.. 10.14.1 The proportional odds assumption.. 10.14.2 The proportional odds model.. 10.14.3 Multinomial regression.. 10.15 Longitudinal data.. 10.16 Binomial regression.. 10.16.1 Adjusted risks.. 10.16.2 Risk differences.. 10.16.3 Problems with binomial models.. 10.17 Propensity scoring.. 10.17.1 Pair-matched propensity scores.. 10.17.2 Stratified propensity scores.. 10.17.3 Weighting by the inverse propensity score.. 10.17.4 Adjusting for the propensity score.. 10.17.5 Deriving the propensity score.. 10.17.6 Propensity score outliers.. 10.17.7 Conduct of the matched design.. 10.17.8 Analysis of the matched design.. 10.17.9 Case studies.. 10.17.10 Interpretation of effects.. 10.17.11 Problems with estimating uncertainty.. 10.17.12 Propensity scores in practice.. 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.. 11.2.2 The hazard function.. 11.3 Estimating the hazard function.. 11.3.1 Kaplan-Meier estimation.. 11.3.2 Person-time estimation.. 11.3.3 Actuarial estimation.. 11.3.4 The cumulative hazard.. 11.4 Probability models.. 11.4.1 The probability density and cumulative distribution functions.. 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 Proportional hazards regression models.. 11.5.1 Comparing two groups.. 11.5.2 Comparing several groups.. 11.5.3 Modelling with a quantitative variable.. 11.5.4 Modelling with several variables.. 11.5.5 Left-censoring.. 11.6 The Cox proportional hazards model.. 11.6.1 Time-dependent covariates.. 11.6.2 Recurrent events.. 11.7 The Weibull proportional hazards model

11.8 Model checking.. 11.8.1 Log cumulative hazard plots.. 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 Competing risk.. 11.9.1 Joint modeling of longitudinal and survival data.. 11.10 Poisson regression.. 11.10.1 Simple regression.. 11.10.2 Multiple regression.. 11.10.3 Comparison of standardised event ratios.. 11.10.4 Routine or registration data.. 11.10.5 Generic data.. 11.10.6 Model checking.. 11.11 Pooled logistic regression.. 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.. 12.2.2 Study quality.. 12.3 A General approach to pooling.. 12.3.1 Inverse variance weighting.. 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 Case studies.. 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.. 12.3.12 Dose-response meta-analysis.. 12.4 Investigating heterogeneity.. 12.4.1 Forest plots.. 12.4.2 Influence plots.. 12.4.3 Sensitivity analyses.. 12.4.4 Meta-regression.. 12.5 Pooling tabular data.. 12.5.1 Inverse variance weighting.. 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.. 12.6 Individual participant data.. 12.7 Dealing with aspects of study quality.. 12.8 Publication bias.. 12.8.1 The funnel plot.. 12.8.2 Consequences of publication bias.. 12.8.3 Correcting for publication bias.. 12.8.4 Other causes of asymmetry in funnel plots

12.9 Advantages and limitations of meta-analysis.. Exercises.. 13 Risk scores And clinical decision rules.. 13.1 Introduction.. 13.1.1 Individual and population level interventions.. 13.1.2 Scope of this chapter.. 13.2 Association and prognosis.. 13.2.1 The concept of discrimination.. 13.2.2 Risk factor thresholds.. 13.2.3 Risk thresholds.. 13.2.4 Odds ratios and discrimination.. 13.3 Risk scores from statistical models.. 13.3.1 Logistic regression.. 13.3.2 Multiple variable risk scores.. 13.3.3 Cox regression.. 13.3.4 Risk thresholds.. 13.3.5 Multiple thresholds.. 13.4 Quantifying discrimination.. 13.4.1 The area under the curve.. 13.4.2 Comparing AUCs.. 13.4.3 Survival data.. 13.4.4 The standardised mean effect size.. 13.4.5 Other measures of discrimination.. 13.5 Calibration.. 13.5.1 Overall calibration.. 13.5.2 Mean calibration.. 13.5.3 Grouped calibration.. 13.5.4 Calibration plots.. 13.6 Recalibration.. 13.6.1 Recalibration of the mean.. 13.6.2 Recalibration of scores in a fixed cohort.. 13.6.3 Recalibration of parameters from a Cox model.. 13.6.4 Recalibration and discrimination.. 13.7 The accuracy of predictions.. 13.7.1 The Brier score.. 13.7.2 Comparison of Brier scores.. 13.8 Assessing an extraneous prognostic variable.. 13.9 Reclassification.. 13.9.1 The integrated discrimination improvement from a fixed cohort.. 13.9.2 The net reclassification improvement from a fixed cohort.. 13.9.3 The integrated discrimination improvement from a variable cohort.. 13.9.4 The net reclassification improvement from a variable cohort.. 13.9.5 Software.. 13.10 Validation.. 13.11 Presentation of risk scores.. 13.11.1 Point scoring.. 13.12 Impact Studies.. Exercises.. 14 Computer-intensive methods.. 14.1 Rationale.. 14.2 The bootstrap.. 14.2.1 Bootstrap distributions.. 14.3 Bootstrap confidence intervals.. 14.3.1 Bootstrap normal intervals.. 14.3.2 Bootstrap percentile intervals.. 14.3.3 Bootstrap bias-corrected intervals.

14.3.4 Bootstrap bias-corrected and accelerated intervals.. 14.3.5 Overview of the worked example.. 14.3.6 Choice of bootstrap interval.. 14.4 Practical issues when bootstrapping.. 14.4.1 Software.. 14.4.2 How many replications should be used?.. 14.4.3 Sensible strategies.. 14.5 Further examples of bootstrapping.. 14.5.1 Complex bootstrap samples.. 14.6 Bootstrap hypothesis testing.. 14.7 Limitations of bootstrapping.. 14.8 Permutation tests.. 14.8.1 Monte Carlo permutation tests.. 14.8.2 Limitations.. 14.9 Missing values.. 14.9.1 Dealing with missing values.. 14.9.2 Types of missingness.. 14.9.3 Complete case analyses.. 14.10 Naive imputation methods.. 14.10.1 Mean imputation.. 14.10.2 Conditional mean and regression imputation.. 14.10.3 Hot deck imputation and predictive mean matching.. 14.10.4 Longitudinal data.. 14.11 Univariate multiple imputation.. 14.11.1 Multiple imputation by regression.. 14.11.2 The three-step process in MI.. 14.11.3 Imputer's and analyst's models.. 14.11.4 Rubin's equations.. 14.11.5 Imputation diagnostics.. 14.11.6 Skewed continuous data.. 14.11.7 Other types of variables.. 14.11.8 How many imputations?.. 14.12 Multivariate multiple imputation.. 14.12.1 Monotone imputation.. 14.12.2 Data augmentation.. 14.12.3 Categorical variables.. 14.12.4 What to do when DA fails.. 14.12.5 Chained equations.. 14.12.6 Longitudinal data.. 14.13 When is it worth imputing?.. Exercises.. Appendix A Materials available on the website for this book.. Appendix B Statistical tables.. Appendix C Additional datasets for exercises.. References.. Index

Highly praised for its broad, practical coverage, the second edition of this popular text incorporated the major statistical models and issues relevant to epidemiological studies. Epidemiology: Study Design and Data Analysis, Third Edition continues to focus on the quantitative aspects of epidemiological research. Updated and expanded, this edition shows students how statistical principles and techniques can help solve epidemiological problems. New to the Third Edition • New chapter on risk scores and clinical decision rules • New chapter on computer-intensive methods, including the bootstrap, permutation tests, and missing value imputation • New sections on binomial regression models, competing risk, information criteria, propensity scoring, and splines • Many more exercises and examples using both Stata and SAS • More than 60 new figures After introducing study design and reviewing all the standard methods, this self-contained book takes students through analytical methods for both general and specific epidemiological study designs, including cohort, case-control, and intervention studies. In addition to classical methods, it now covers modern methods that exploit the enormous power of contemporary computers. The book also addresses the problem of determining the appropriate size for a study, discusses statistical modeling in epidemiology, covers methods for comparing and summarizing the evidence from several studies, and explains how to use statistical models in risk forecasting and assessing new biomarkers. eng