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| Introduction | |
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| Clinical Questions and Clinical Epidemiology | |
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| Health Outcomes | |
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| The Scientific Basis for Clinical Medicine | |
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| Basic Principles | |
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| Variables | |
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| Numbers and Probability | |
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| Populations and Samples | |
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| Bias (Systematic Error) | |
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| Selection Bias | |
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| Measurement Bias | |
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| Confounding | |
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| Chance | |
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| The Effects of Bias and Chance Are Cumulative | |
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| Internal and External Validity | |
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| Information and Decisions | |
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| Organization of this Book | |
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| Frequency | |
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| Are Words Suitable Substitutes for Numbers? | |
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| Prevalence and Incidence | |
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| Prevalence | |
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| Incidence | |
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| Prevalence and Incidence in Relation to Time | |
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| Relationships Among Prevalence, Incidence, and Duration of Disease | |
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| Some other Rates | |
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| Studies of Prevalence and Incidence | |
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| Prevalence Studies | |
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| Incidence Studies | |
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| Cumulative Incidence | |
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| Incidence Density (Person-Years) | |
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| Basic Elements of Frequency Studies | |
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| What Is a Case? Defining the Numerator | |
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| What Is the Population? Defining the Denominator | |
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| Does the Study Sample Represent the Population? | |
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| Distribution of Disease by Time, Place, and Person | |
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| Time | |
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| Place | |
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| Person | |
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| Uses of Prevalence Studies | |
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| What Are Prevalence Studies Good For? | |
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| What Are Prevalence Studies Not Particularly Good For? | |
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| Abnormality | |
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| Types of Data | |
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| Nominal Data | |
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| Ordinal Data | |
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| Interval Data | |
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| Performance of Measurements | |
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| Validity | |
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| Content Validity | |
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| Criterion Validity | |
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| Construct Validity | |
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| Reliability | |
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| Range | |
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| Responsiveness | |
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| Interpretability | |
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| Variation | |
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| Variation Resulting from Measurement | |
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| Variation Resulting from Biologic Differences | |
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| Total Variation | |
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| Effects of Variation | |
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| Distributions | |
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| Describing Distributions | |
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| Actual Distributions | |
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| The Normal Distribution | |
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| Criteria for Abnormality | |
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| Abnormal = Unusual | |
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| Abnormal = Associated with Disease | |
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| Abnormal = Treating the Condition Leads to a Better Clinical Outcome | |
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| Regression to the Mean | |
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| Risk: Basic Principles | |
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| Risk Factors | |
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| Recognizing Risk | |
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| Long Latency | |
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| Immediate Versus Distant Causes | |
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| Common Exposure to Risk Factors | |
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| Low Incidence of Disease | |
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| Small Risk | |
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| Multiple Causes and Multiple Effects | |
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| Risk Factors May or May Not Be Causal | |
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| Predicting Risk | |
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| Combining Multiple Risk Factors to Predict Risk | |
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| Risk Prediction in Individual Patients and Groups | |
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| Evaluating Risk Prediction Tools | |
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| Calibration | |
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| Discrimination | |
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| Sensitivity and Specificity of a Risk Prediction Tool | |
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| Risk Stratification | |
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| Why Risk Prediction Tools Do Not Discriminate Well Among Individuals | |
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| Clinical Uses of Risk Factors and Risk Prediction Tools | |
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| Risk Factors and Pretest Probability for Diagnostic Testing | |
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| Using Risk Factors to Choose Treatment | |
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| Risk Stratification for Screening Programs | |
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| Removing Risk Factors to Prevent Disease | |
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| Risk: Exposure to Disease | |
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| Studies of Risk | |
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| When Experiments Are Not Possible or Ethical | |
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| Cohorts | |
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| Cohort Studies | |
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| Prospective and Historical Cohort Studies | |
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| Prospective Cohort Studies | |
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| Historical Cohort Studies Using Medical Databases | |
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| Case-Cohort Studies | |
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| Advantages and Disadvantages of Cohort Studies | |
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| Ways to Express and Compare Risk | |
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| Absolute Risk | |
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| Attributable Risk | |
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| Relative Risk | |
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| Interpreting Attributable and Relative Risk | |
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| Population Risk | |
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| Taking other Variables into Account | |
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| Extraneous Variables | |
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| Simple Descriptions of Risk | |
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| Confounding | |
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| Working Definition | |
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| Potential Confounders | |
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| Confirming Confounding | |
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| Control of Confounding | |
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| Randomization | |
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| Restriction | |
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| Matching | |
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| Stratification | |
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| Standardization | |
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| Multivariable Adjustment | |
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| Overall Strategy for Control of Confounding | |
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| Observational Studies and Cause | |
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| Effect Modification | |
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| Risk: From Disease to Exposure | |
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| Case-Control Studies | |
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| Design of Case-Control Studies | |
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| Selecting Cases | |
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| Selecting Controls | |
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| The Population Approach | |
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| The Cohort Approach | |
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| Hospital and Community Controls | |
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| Multiple Control Groups | |
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| Multiple Controls per Case | |
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| Matching | |
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| Measuring Exposure | |
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| Multiple Exposures | |
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| The Odds Ratio: An Estimate of Relative Risk | |
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| Controlling for Extraneous Variables | |
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| Investigation of A Disease Outbreak | |
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| Prognosis | |
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| Differences in Risk and Prognostic Factors | |
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| The Patients Ate Different | |
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| The Outcomes Are Different | |
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| The Rates Are Different | |
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| The Factors May be Different | |
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| Clinical Course and Natural History of Disease | |
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| Elements of Prognostic Studies | |
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| Patient Sample | |
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| Zero Time | |
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| Follow-Up | |
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| Outcomes of Disease | |
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| Describing Prognosis | |
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| A Trade-Off: Simplicity versus More Information | |
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| Survival Analysis | |
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| Survival of a Cohort | |
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| Survival Curves | |
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| Interpreting Survival Curves | |
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| Identifying Prognostic Factors | |
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| Case Series | |
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| Clinical Prediction Rules | |
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| Bias in Cohort Studies | |
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| Sampling Bias | |
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| Migration Bias | |
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| Measurement Bias | |
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| Bias from "Non-differential" Misclassification | |
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| Bias, Perhaps, but does it Matter? | |
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| Sensitivity Analysis | |
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| Diagnosis | |
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| Simplifying Data | |
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| The Accuracy of a Test Result | |
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| The Gold Standard | |
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| Lack of Information on Negative Tests | |
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| Lack of Information on Test Results in the Nondiseased | |
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| Lack of Objective Standards for Disease | |
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| Consequences of Imperfect Gold Standards | |
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| Sensitivity and Specificity | |
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| Definitions | |
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| Use of Sensitive Tests | |
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| Use of Specific Tests | |
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| Trade-Offs between Sensitivity and Specificity | |
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| The Receiver Operator Characteristic (ROC) Curve | |
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| Establishing Sensitivity and Specificity | |
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| Spectrum of Patients | |
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| Bias | |
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| Chance | |
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| Predictive Value | |
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| Definitions | |
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| Determinants of Predictive Value | |
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| Estimating Prevalence (Pretest Probability) | |
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| Increasing the Pretest Probability of Disease | |
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| Specifics of the Clinical Situation | |
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| Selected Demographic Groups | |
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| Referral Process | |
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| Implications for Interpreting the Medical Literature | |
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| Likelihood Ratios | |
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| Odds | |
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| Definitions | |
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| Use of Likelihood Ratios | |
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| Why Use Likelihood Ratios? | |
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| Calculating Likelihood Ratios | |
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| Multiple Tests | |
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| Parallel Testing | |
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| Clinical Prediction Rules | |
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| Serial Testing | |
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| Serial Likelihood Ratios | |
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| Assumption of Independence | |
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| Treatment | |
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| Ideas and Evidence | |
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| Ideas | |
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| Testing Ideas | |
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| Studies of Treatment Effects | |
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| Observational and Experimental Studies of Treatment Effects | |
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| Randomized Controlled Trials | |
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| Ethics | |
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| Sampling | |
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| Intervention | |
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| Comparison Groups | |
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| Allocating Treatment | |
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| Differences Arising after Randomization | |
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| Patients May Not Have the Disease Being Studied | |
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| Compliance | |
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| Cross-over | |
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| Cointerventions | |
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| Blinding | |
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| Assessment of Outcomes | |
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| Efficacy and Effectiveness | |
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| Intention-to-Treat and Explanatory Trials | |
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| Superiority, Equivalence, and Non-Inferiority | |
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| Variations on Basic Randomized Trials | |
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| Tailoring the Results of Trials to Individual Patients | |
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| Subgroups | |
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| Effectiveness in Individual Patients | |
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| Trials of N = 1 | |
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| Alternatives to Randomized Controlled Trials | |
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| Limitations of Randomized Trials | |
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| Observational Studies of Interventions | |
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| Clinical Databases | |
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| Randomized versus Observational Studies? | |
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| Phases of Clinical Trials | |
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| Prevention | |
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| Preventive Activities in Clinical Settings | |
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| Types of Clinical Prevention | |
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| Immunization | |
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| Screening | |
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| Behavioral Counseling (Lifestyle Changes) | |
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| Chemoprevention | |
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| Levels of Prevention | |
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| Primary Prevention | |