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Foundations for Evaluating Clinical Literature

Foundations for Evaluating Clinical Literature. Outline . Definitions Study Design Sampling Bias Reliability and validity. Research Areas. Clinical Research (humans) Diagnosis Frequency (prevalence/incidence) Risk factors Prognosis Treatment Prevention Laboratory Research (basic)

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Foundations for Evaluating Clinical Literature

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  1. Foundations for Evaluating Clinical Literature

  2. Outline • Definitions • Study Design • Sampling • Bias • Reliability and validity

  3. Research Areas • Clinical Research (humans) • Diagnosis • Frequency (prevalence/incidence) • Risk factors • Prognosis • Treatment • Prevention • Laboratory Research (basic) • Typically animal models • Human and animal cell cultures and biologic samples • Genetic material • Translational Research • New area (relatively)

  4. Classification of Studies • Observational • Individuals • Test cause-effect hypotheses • Uncontrolled assignment to study groups (naturalistic) • Experimental • Individuals • Test cause-effect hypotheses • Controlled assignment to study groups • Descriptive • Populations • Prevalence/incidence • Distribution (disease, risks, demographics, etc.) Survey Case-control Cohort Clinical trial

  5. Causes and Effects X Independent variable “cause” Examples? Y Dependent variable “effect” Examples? Probabilistic causality

  6. Study Design • Laboratory experiment • Causality: chemical X --> cancer • Method: 100 mice randomized to exposed and unexposed groups (e.g. 50 each) • Follow up for some time • Measure cancer incidence rate in the two groups • Proof of causation

  7. Study Design • Experimental • Randomization --> independent variable (drug X) --> Dependent variable (outcome/endpoint Y) • Analytic/observational • Independent Variable (risk factor) --> Dependent Variable (outcome)

  8. Sampling Target Sample Inference/generalization Study hypothesis testing Intended Sample Actual Sample Potential bias (e.g. non-responder)

  9. Measurement • Two types of issues to expect: • Random (instrument sensitivity) • Systematic (instrument bias)

  10. Reliability vs. Variability Observer Subject Instrument

  11. Observer Reliability Observer 2 (or 1 but diff time) Observer 1 Percent agreement = (a+d)/N Kappa = (Observed % agr - Expected % agr) / (100%-Expected % agreement)

  12. Validity/Accuracy Outcome condition (disease) Test Sensitivity = TP/(TP+FN) PPV = TP/(TP+FP) Specificity = TN/(FP+TN) NPV = TN/(TN+FN)

  13. Summary • Things to keep an eye out for when designing or evaluating studies: • Hypothesis • Sample (size and sampling strategy) • Bias (sampling or measurement) • Instruments (reliability and validity)

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