Preface
The Julius Center
About the Authors
Contributors
Acknowledgements
CHAPTER 1 INTRODUCTION
Clinical epidemiology
Research relevant to patient care
Epidemiologic study design
Theoretical design
Design of data collection
Design of data analysis
Diagnostic, etiologic, prognostic and intervention research
Shared and disparate aspects of diagnostic and prognostic research
Shared and disparate aspects of etiologic and prognostic research
Moving from research to practice: relevance and generalizability
CHAPTER 2 Etiologic research
Theoretical design
Confounding
Handling of confounding
Causality
Modification and interaction
Measurement of modification
Modifiers and confounders
Design of data collection: cohorts, cases or experimentation
Design of data analysis: measures of association
Common etiologic questions in clinical epidemiology
Etiologic research: worked out example
CHAPTER 3 Diagnostic research
Diagnosis in clinical practice
From diagnosis in clinical practice to diagnostic research
Diagnostic research versus test research
Rational diagnostic research
Theoretical design
Design of data collection
Bias in diagnostic research
Design of data analysis
Worked-out example
CHAPTER 4 Prognostic research
Prognosis in clinical practice
Motive and aim of prognosis
The format of prognoses
Approaches to prognostication
Prognostication is a multivariable process
Added prognostic value
From prognosis in clinical practice to prognostic research
The predictive nature of prognostic research
Appraisal of prevailing prognostic research
Rational prognostic research
Theoretical design
Design of data collection
Bias in prognostic research
Design of data analysis
Worked-out example
CHAPTER 5 Intervention research: Main effects
Learning about effects of intervention
Natural history
Extraneous effects
Observer effects
The treatment effect
Comparability of natural history
Randomization
Comparability of extraneous effects
Comparability of observations
Limits to trials
The randomized trial as a paradigm for etiologic research
CHAPTER 6 Intervention research: side effects
Research on side effects of interventions
Studies on side effects of interventions: causal research
Type A and type B side effects
Theoretical design
Design of data collection
Comparability of observations in observational research on side effects
Comparability of extraneous effects in observational research on side effects
Comparability of natural history effects in observational research on side effects
Methods to limit confounding in observational studies on side effects of interventions
Methods to limit confounding in the design of data collection
Methods to limit confounding in the design of data collection
Health care databases as framework for research on side effects of interventions
CHAPTER 7 Design of data collection
Time
Census or sampling
Experimental or observational
Taxonomy of epidemiologic data collection
CHAPTER 8 Cohort and cross-sectional studies
Timing of the association relative to the timing of data collection
Causal and descriptive cohort studies
Experimental cohort studies
Cross-sectional studies
Ecologic studies
Cohort studies using routine care data
Limitations to cohort studies
Worked out example (SMART)
CHAPTER 9 Case-control studies
Rationale and essence of case-control studies
A brief history of case-control studies in clinical research
Theoretical design
Design of data collection
Swimming-pool, a life-guard chair and a net
Identification of cases
Sampling of controls: the study base (or “swimming-pool”) principle
Specific types of control series
Multiple control series?
Matching of cases and controls?
Design of data-analysis in case-control studies
Case-cohort studies
Case-crossover studies
Case-control studies with no controls
Advantages and limitations of case-control studies
Worked-out example
CHAPTER 10 Randomized trials
‘Regular’ parallel, factorial, cross-over and cluster trials
Participants
Treatment allocation and randomization
Informed consent
Blinding
Outcome
Design of data analysis
CHAPTER 11 Meta-analyses
Rationale of meta-analysis
Principles of meta-analysis
Theoretical design and research question
Design of data collection
Critical appraisal
Design of data analysis
Reporting results from meta-analysis
Inferences from meta-analysis
CHAPTER 12 Clinical Epidemiologic Data Analysis
Measures of Disease Frequency: Incidence and Prevalence
Data-analytical strategies in clinical epidemiology research
The relationship between determinant and outcome
Adjustment for confounding
Regression analysis
Frequentists and Baysians
References
Index
