Clinical Epidemiology

Höfundur: Grant S. Fletcher (Útgáfa: 6)
Clinical Epidemiology

Kaup valmöguleikar

Now in its Sixth Edition, Clinical Epidemiology: The Essentials is a comprehensive, concise, and clinically oriented introduction to the subject of epidemiology. Written by expert educators, this approachable, informative 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.

Nánar um bókina

Útgefandi
Wolters Kluwer Health
ISBN
9781975284237
Print ISBN
9781975140984
Format
ePub
Útgáfa
6
Höfundar
Grant S. Fletcher
Tungumál
English
Útgefið
2020-02-27
Prent takmörkun á líftíma
10
Prent takmörkun
2
Afritunar takmörkun
2

Kaflar

  • Cover
  • Title Page
  • Copyright
  • Preface
  • Acknowledgments
  • Contents in Brief
  • Contents
  • 1 Introduction
  • Clinical Questions and Clinical Epidemiology
  • Health Outcomes
  • The Scientific Basis for Clinical Medicine
  • Basic Principles
  • Variables
  • Numbers and Probability
  • Populations and Samples
  • Bias (Systematic Error)
  • Chance
  • The Effects of Bias and Chance Are Cumulative
  • Internal and External Validity
  • Information and Decisions
  • Organization of This Book
  • Chapter 1: Multiple Choice Questions
  • 2 Frequency
  • Are Words Suitable Substitutes for Numbers?
  • Prevalence and Incidence
  • Prevalence
  • Incidence
  • Prevalence and Incidence in Relation to Time
  • Relationships Among Prevalence, Incidence, and Duration of Disease
  • Some Other Rates
  • Studies of Prevalence and Incidence
  • Prevalence Studies
  • Incidence Studies
  • Cumulative Incidence
  • Incidence Density (Person-Years)
  • Basic Elements of Frequency Studies
  • What Is a Case? Defining the Numerator
  • What Is the Population? Defining the Denominator
  • Does the Study Sample Represent the Population?
  • Distribution of Disease by Time, Place, and Person
  • Time
  • Place
  • Person
  • Uses of Prevalence Studies
  • What Are Prevalence Studies Good for?
  • What Are Prevalence Studies Not Particularly Good for?
  • Chapter 2: Multiple Choice Questions
  • 3 Abnormality
  • Types of Data
  • Nominal Data
  • Ordinal Data
  • Interval Data
  • Performance of Measurements
  • Validity
  • Reliability
  • Range
  • Responsiveness
  • Interpretability
  • Variation
  • Variation Resulting from Measurement
  • Variation Resulting from Biologic Differences
  • Total Variation
  • Effects of Variation
  • Distributions
  • Describing Distributions
  • Actual Distributions
  • The Normal Distribution
  • Criteria for Abnormality
  • Abnormal = Unusual
  • Abnormal = Biologic Dysfunction
  • Abnormal = Illness
  • Abnormal = Treating the Condition Leads to a Better Clinical Outcome
  • Regression to the Mean
  • Chapter 3: Multiple Choice Questions
  • 4 Diagnosis
  • Simplifying Data
  • The Accuracy of a Test Result
  • The Gold Standard
  • Sensitivity and Specificity
  • Definitions
  • Use of Sensitive Tests
  • Use of Specific Tests
  • Trade-Offs Between Sensitivity and Specificity
  • The Receiver Operator Characteristic (ROC) Curve
  • Studies of Diagnostic Tests
  • Spectrum of Patients—the Study Population
  • Bias
  • Chance
  • Imperfect Gold Standards
  • Predictive Value
  • Definitions
  • Determinants of Predictive Value
  • Estimating Prevalence (Pretest Probability)
  • Implications for Interpreting the Medical Literature
  • Likelihood Ratios
  • Odds
  • Definitions
  • Use of Likelihood Ratios
  • Why Use Likelihood Ratios?
  • Calculating Likelihood Ratios
  • Multiple Tests
  • Parallel Testing
  • Clinical Prediction Rules
  • Serial Testing
  • Serial Likelihood Ratios
  • Assumption of Independence
  • Chapter 4: Multiple Choice Questions
  • 5 Risk: Basic Principles
  • Risk Measurement
  • Risk Factors
  • Recognizing Risk Factors
  • Long Latency
  • Immediate Versus Distant Causes
  • Common Exposure to Risk Factors
  • Low Incidence of Disease
  • Small Risk
  • Multiple Causes and Multiple Effects
  • Risk Factors May or May Not Be Causal
  • Risk Prediction Models
  • Combining Multiple Factors
  • Evaluating Risk Prediction Tools
  • Discrimination
  • Calibration
  • Validating Models
  • External Validation
  • Comparing Models
  • Assessing Models in Clinical Practice
  • Risk Stratification
  • Clinical Uses of Risk Factors, Prognostic Factors, and Risk Prediction Tools
  • Risk Prediction and Pretest Probability for Diagnostic Testing
  • Using Risk Factors to Choose Treatment
  • Risk Stratification for Screening Programs
  • Removing Risk Factors to Prevent Disease
  • Chapter 5: Multiple Choice Questions
  • 6 Risk: Exposure to Disease
  • Studies of Risk
  • When Experiments Are Not Possible or Ethical
  • Cohorts
  • Cohort Studies
  • Prospective and Historical Cohort Studies
  • Advantages and Disadvantages of Cohort Studies
  • Ways to Express and Compare Risk
  • Absolute Risk
  • Attributable Risk
  • Relative Risk
  • Interpreting Attributable and Relative Risk
  • Population Risk
  • Taking Other Variables into Account
  • Extraneous Variables
  • Simple Descriptions of Risk
  • Confounding
  • Working Definition
  • Potential Confounders
  • Confirming Confounding
  • Control of Confounding
  • Randomization
  • Restriction
  • Matching
  • Stratification
  • Standardization
  • Multivariable Adjustment
  • Overall Strategy for Control of Confounding
  • Observational Studies and Cause
  • Effect Modification
  • Mendelian Randomization
  • Chapter 6: Multiple Choice Questions
  • 7 Risk: From Disease to Exposure
  • Case-Control Studies
  • Design of Case-Control Studies
  • The Source Population
  • Selecting Cases
  • Selecting Controls
  • Measuring Exposure
  • The Odds Ratio: An Estimate of Relative Risk
  • Odds Ratio Calculation
  • Odds Ratio as an Indirect Estimate of Relative Risk
  • Odds Ratio as a Direct Estimate of Relative Risk
  • Controlling for Extraneous Variables
  • Investigation of a Disease Outbreak
  • Chapter 7: Multiple Choice Questions
  • 8 Prognosis
  • Differences in Risk and Prognostic Factors
  • The Patients Are Different
  • The Outcomes Are Different
  • The Rates Are Different
  • The Factors May be Different
  • Clinical Course and Natural History of Disease
  • Elements of Prognostic Studies
  • Patient Sample
  • Zero Time
  • Follow-Up
  • Outcomes of Disease
  • Describing Prognosis
  • A Trade-Off: Simplicity Versus More Information
  • Survival Analysis
  • Survival of a Cohort
  • Survival Curves
  • Interpreting Survival Curves
  • Identifying Prognostic Factors
  • Case Series
  • Clinical Prediction Rules
  • Bias in Cohort Studies
  • Sampling Bias
  • Migration Bias
  • Measurement Bias
  • Bias from “Non-differential” Misclassification
  • Bias from Missing Data
  • Bias, Perhaps, But Does It Matter?
  • Sensitivity Analysis
  • Chapter 8: Multiple Choice Questions
  • 9 Treatment
  • Ideas and Evidence
  • Ideas
  • Testing Ideas
  • Studies of Treatment Effects
  • Observational and Experimental Studies of Treatment Effects
  • Randomized Controlled Trials
  • Ethics
  • Sampling
  • Intervention
  • Comparison Groups
  • Allocating Treatment
  • Differences Arising After Randomization
  • Blinding
  • Assessment of Outcomes
  • Efficacy and Effectiveness
  • Intention-to-Treat and Explanatory Trials
  • Superiority, Equivalence, and Noninferiority
  • Variations on Basic Randomized Trials
  • Tailoring the Results of Trials to Individual Patients
  • Subgroups
  • Effectiveness in Individual Patients
  • N of 1 Trials
  • Alternatives to Randomized Controlled Trials
  • Limitations of Randomized Trials
  • Observational Studies of Interventions
  • Clinical Databases
  • Randomized Versus Observational Studies?
  • Phases of Clinical Trials
  • Chapter 9: Multiple Choice Questions
  • 10 Prevention
  • Preventive Activities in Clinical Settings
  • Types of Clinical Prevention
  • Levels of Prevention
  • Primary Prevention
  • Secondary Prevention
  • Tertiary Prevention
  • Confusion About Primary, Secondary, and Tertiary Prevention
  • Scientific Approach to Clinical Prevention
  • Burden of Suffering
  • Effectiveness of Treatment
  • Treatment in Primary Prevention
  • Treatment in Secondary Prevention
  • Treatment in Tertiary Prevention
  • Methodologic Issues in Evaluating Screening Programs
  • Prevalence and Incidence Screens
  • Special Biases
  • Performance of Screening Tests
  • High Sensitivity and Specificity
  • Detection and Incidence Methods for Calculating Sensitivity
  • Low Positive Predictive Value
  • Simplicity and Low Cost
  • Safety
  • Acceptable to Patients and Clinicians
  • Unintended Consequences of Screening
  • Risk of False-Positive Result
  • Risk of Negative Labeling Effect
  • Risk of Overdiagnosis (Pseudodisease) in Cancer Screening
  • Incidentalomas
  • Changes in Screening Tests and Treatments Over Time
  • Weighing Benefits Against Harms of Prevention
  • Chapter 10: Multiple Choice Questions
  • 11 Chance
  • Two Approaches to Chance
  • Hypothesis Testing
  • False-Positive and False-Negative Statistical Results
  • Concluding That a Treatment Works
  • Dichotomous and Exact P Values
  • Statistical Significance and Clinical Importance
  • Statistical Tests
  • Concluding That a Treatment Does Not Work
  • How Many Study Patients Are Enough?
  • Statistical Power
  • Estimating Sample Size Requirements
  • Point Estimates and Confidence Intervals
  • Statistical Power After a Study Is Completed
  • Detecting Rare Events
  • Multiple Comparisons
  • Subgroup Analysis
  • Multiple Outcomes
  • Noninferiority Studies
  • Multivariable Methods
  • Bayesian Reasoning
  • Chapter 11: Multiple Choice Questions
  • 12 Cause
  • Basic Principles
  • Single Causes
  • Multiple Causes
  • Proximity of Cause to Effect
  • Indirect Evidence for Cause
  • Examining Individual Studies
  • Hierarchy of Research Designs
  • The Body of Evidence for and Against Cause
  • Does Cause Precede Effect?
  • Strength of the Association
  • Dose–Response Relationships
  • Reversible Associations
  • Consistency
  • Biologic Plausibility
  • Specificity
  • Analogy
  • Aggregate Risk Studies
  • Modeling
  • Weighing the Evidence
  • Chapter 12: Multiple Choice Questions
  • 13 Summarizing the Evidence
  • Traditional Reviews
  • Systematic Reviews
  • Defining a Specific Question
  • Selecting Studies
  • Assessing Study Quality and Characteristics
  • Summarizing Results
  • Combining Studies in Meta-Analyses
  • Are the Studies Similar Enough to Justify Combining?
  • How Are the Results Pooled?
  • Identifying Reasons for Heterogeneity
  • Additional Meta-Analysis Methods
  • Patient-Level Meta-Analysis
  • Network Meta-Analysis
  • Cumulative Meta-Analyses
  • Systematic Reviews of Observational and Diagnostic Studies
  • Strengths and Weaknesses of Meta-Analyses
  • Chapter 13: Multiple Choice Questions
  • 14 Knowledge Management
  • Basic Principles
  • Do It Yourself or Delegate?
  • Which Medium?
  • Grading Information
  • Misleading Reports of Research Findings
  • Looking Up Answers to Clinical Questions
  • Solutions
  • Surveillance on New Developments
  • Journals
  • “Reading” Journals
  • Guiding Patients’ Quest for Health Information
  • Putting Knowledge Management into Practice
  • Chapter 14: Multiple Choice Questions
  • Appendix B: Additional Readings
  • Index