Clinical epidemiology : the essentials

Now in its Fifth Edition, Clinical Epidemiology: The Essentials is a comprehensive, concise, and clinically oriented introduction to the subject of epidemiology. Written by expert educators, this text introduces students to the principles of evidence-based medicine that will help them develop and apply methods of clinical observation in order to form accurate conclusions. The Fifth Edition includes more complete coverage of systematic reviews and knowledge management, as well as other key topics such as abnormality, diagnosis, frequency and risk, prognosis, treatment, prevention, chance, studying cases and cause.

• Chapter 1 Introduction p. 1
• Clinical Questions and Clinical Epidemiology p. 2
• Health Outcomes p. 2
• The Scientific Basis for Clinical Medicine p. 3
• Basic Principles p. 6
• Variables p. 6
• Numbers and Probability p. 6
• Populations and Samples p. 6
• Bias (Systematic Error) p. 7
• Selection Bias p. 7
• Measurement Bias p. 8
• Confounding p. 8
• Chance p. 10
• The Effects of Bias and Chance Are Cumulative p. 10
• Internal and External Validity p. 11
• Information and Decisions p. 12
• Organization of this Book p. 12
• Chapter 2 Frequency p. 17
• Are Words Suitable Substitutes for Numbers? p. 18
• Prevalence and Incidence p. 18
• Prevalence p. 18
• Incidence p. 18
• Prevalence and Incidence in Relation to Time p. 19
• Relationships Among Prevalence, Incidence, and Duration of Disease p. 19
• Some other Rates p. 20
• Studies of Prevalence and Incidence p. 21
• Prevalence Studies p. 21
• Incidence Studies p. 21
• Cumulative Incidence p. 21
• Incidence Density (Person-Years) p. 22
• Basic Elements of Frequency Studies p. 23
• What Is a Case? Defining the Numerator p. 23
• What Is the Population? Defining the Denominator p. 25
• Does the Study Sample Represent the Population? p. 25
• Distribution of Disease by Time, Place, and Person p. 25
• Time p. 26
• Place p. 27
• Person p. 27
• Uses of Prevalence Studies p. 28
• What Are Prevalence Studies Good For? p. 28
• What Are Prevalence Studies Not Particularly Good For? p. 28
• Chapter 3 Abnormality p. 31
• Types of Data p. 32
• Nominal Data p. 32
• Ordinal Data p. 32
• Interval Data p. 32
• Performance of Measurements p. 33
• Validity p. 33
• Content Validity p. 33
• Criterion Validity p. 33
• Construct Validity p. 34
• Reliability p. 34
• Range p. 34
• Responsiveness p. 34
• Interpretability p. 35
• Variation p. 35
• Variation Resulting from Measurement p. 35
• Variation Resulting from Biologic Differences p. 36
• Total Variation p. 37
• Effects of Variation p. 37
• Distributions p. 38
• Describing Distributions p. 38
• Actual Distributions p. 39
• The Normal Distribution p. 40
• Criteria for Abnormality p. 41
• Abnormal = Unusual p. 42
• Abnormal = Associated with Disease p. 43
• Abnormal = Treating the Condition Leads to a Better Clinical Outcome p. 43
• Regression to the Mean p. 45
• Chapter 4 Risk: Basic Principles p. 50
• Risk Factors p. 51
• Recognizing Risk p. 51
• Long Latency p. 51
• Immediate Versus Distant Causes p. 51
• Common Exposure to Risk Factors p. 52
• Low Incidence of Disease p. 52
• Small Risk p. 52
• Multiple Causes and Multiple Effects p. 52
• Risk Factors May or May Not Be Causal p. 53
• Predicting Risk p. 54
• Combining Multiple Risk Factors to Predict Risk p. 54
• Risk Prediction in Individual Patients and Groups p. 54
• Evaluating Risk Prediction Tools p. 56
• Calibration p. 56
• Discrimination p. 56
• Sensitivity and Specificity of a Risk Prediction Tool p. 56
• Risk Stratification p. 57
• Why Risk Prediction Tools Do Not Discriminate Well Among Individuals p. 57
• Clinical Uses of Risk Factors and Risk Prediction Tools p. 58
• Risk Factors and Pretest Probability for Diagnostic Testing p. 58
• Using Risk Factors to Choose Treatment p. 58
• Risk Stratification for Screening Programs p. 58
• Removing Risk Factors to Prevent Disease p. 59
• Chapter 5 Risk: Exposure to Disease p. 61
• Studies of Risk p. 61
• When Experiments Are Not Possible or Ethical p. 61
• Cohorts p. 62
• Cohort Studies p. 62
• Prospective and Historical Cohort Studies p. 63
• Prospective Cohort Studies p. 63
• Historical Cohort Studies Using Medical Databases p. 64
• Case-Cohort Studies p. 65
• Advantages and Disadvantages of Cohort Studies p. 65
• Ways to Express and Compare Risk p. 67
• Absolute Risk p. 67
• Attributable Risk p. 68
• Relative Risk p. 68
• Interpreting Attributable and Relative Risk p. 68
• Population Risk p. 69
• Taking other Variables into Account p. 71
• Extraneous Variables p. 71
• Simple Descriptions of Risk p. 71
• Confounding p. 71
• Working Definition p. 72
• Potential Confounders p. 72
• Confirming Confounding p. 72
• Control of Confounding p. 72
• Randomization p. 73
• Restriction p. 73
• Matching p. 74
• Stratification p. 74
• Standardization p. 75
• Multivariable Adjustment p. 75
• Overall Strategy for Control of Confounding p. 75
• Observational Studies and Cause p. 76
• Effect Modification p. 76
• Chapter 6 Risk: From Disease to Exposure p. 80
• Case-Control Studies p. 81
• Design of Case-Control Studies p. 83
• Selecting Cases p. 83
• Selecting Controls p. 83
• The Population Approach p. 83
• The Cohort Approach p. 84
• Hospital and Community Controls p. 84
• Multiple Control Groups p. 84
• Multiple Controls per Case p. 85
• Matching p. 85
• Measuring Exposure p. 85
• Multiple Exposures p. 87
• The Odds Ratio: An Estimate of Relative Risk p. 87
• Controlling for Extraneous Variables p. 88
• Investigation of A Disease Outbreak p. 89
• Chapter 7 Prognosis p. 93
• Differences in Risk and Prognostic Factors p. 93
• The Patients Ate Different p. 94
• The Outcomes Are Different p. 94
• The Rates Are Different p. 94
• The Factors May be Different p. 94
• Clinical Course and Natural History of Disease p. 94
• Elements of Prognostic Studies p. 95
• Patient Sample p. 95
• Zero Time p. 96
• Follow-Up p. 96
• Outcomes of Disease p. 96
• Describing Prognosis p. 97
• A Trade-Off: Simplicity versus More Information p. 97
• Survival Analysis p. 97
• Survival of a Cohort p. 97
• Survival Curves p. 98
• Interpreting Survival Curves p. 100
• Identifying Prognostic Factors p. 100
• Case Series p. 101
• Clinical Prediction Rules p. 102
• Bias in Cohort Studies p. 102
• Sampling Bias p. 103
• Migration Bias p. 103
• Measurement Bias p. 104
• Bias from "Non-differential" Misclassification p. 104
• Bias, Perhaps, but does it Matter? p. 104
• Sensitivity Analysis p. 104
• Chapter 8 Diagnosis p. 108
• Simplifying Data p. 108
• The Accuracy of a Test Result p. 109
• The Gold Standard p. 109
• Lack of Information on Negative Tests p. 110
• Lack of Information on Test Results in the Nondiseased p. 110
• Lack of Objective Standards for Disease p. 110
• Consequences of Imperfect Gold Standards p. 111
• Sensitivity and Specificity p. 111
• Definitions p. 113
• Use of Sensitive Tests p. 113
• Use of Specific Tests p. 113
• Trade-Offs between Sensitivity and Specificity p. 113
• The Receiver Operator Characteristic (ROC) Curve p. 114
• Establishing Sensitivity and Specificity p. 115
• Spectrum of Patients p. 116
• Bias p. 116
• Chance p. 117
• Predictive Value p. 117
• Definitions p. 117
• Determinants of Predictive Value p. 118
• Estimating Prevalence (Pretest Probability) p. 119
• Increasing the Pretest Probability of Disease p. 120
• Specifics of the Clinical Situation p. 120
• Selected Demographic Groups p. 120
• Referral Process p. 120
• Implications for Interpreting the Medical Literature p. 122
• Likelihood Ratios p. 122
• Odds p. 122
• Definitions p. 122
• Use of Likelihood Ratios p. 122
• Why Use Likelihood Ratios? p. 123
• Calculating Likelihood Ratios p. 124
• Multiple Tests p. 125
• Parallel Testing p. 126
• Clinical Prediction Rules p. 127
• Serial Testing p. 128
• Serial Likelihood Ratios p. 128
• Assumption of Independence p. 129
• Chapter 9 Treatment p. 132
• Ideas and Evidence p. 132
• Ideas p. 132
• Testing Ideas p. 133
• Studies of Treatment Effects p. 134
• Observational and Experimental Studies of Treatment Effects p. 134
• Randomized Controlled Trials p. 134
• Ethics p. 135
• Sampling p. 135
• Intervention p. 136
• Comparison Groups p. 138
• Allocating Treatment p. 139
• Differences Arising after Randomization p. 139
• Patients May Not Have the Disease Being Studied p. 140
• Compliance p. 140
• Cross-over p. 141
• Cointerventions p. 141
• Blinding p. 141
• Assessment of Outcomes p. 142
• Efficacy and Effectiveness p. 143
• Intention-to-Treat and Explanatory Trials p. 144
• Superiority, Equivalence, and Non-Inferiority p. 145
• Variations on Basic Randomized Trials p. 145
• Tailoring the Results of Trials to Individual Patients p. 146
• Subgroups p. 146
• Effectiveness in Individual Patients p. 146
• Trials of N = 1 p. 146
• Alternatives to Randomized Controlled Trials p. 147
• Limitations of Randomized Trials p. 147
• Observational Studies of Interventions p. 147
• Clinical Databases p. 148
• Randomized versus Observational Studies? p. 148
• Phases of Clinical Trials p. 148
• Chapter 10 Prevention p. 152
• Preventive Activities in Clinical Settings p. 152
• Types of Clinical Prevention p. 152
• Immunization p. 153
• Screening p. 153
• Behavioral Counseling (Lifestyle Changes) p. 153
• Chemoprevention p. 153
• Levels of Prevention p. 153
• Primary Prevention p. 153