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Foundations for Evaluating Clinical Literature: Study Design, Sampling, Bias, and Reliability

Understand and assess clinical research methods through definitions, study types, sampling techniques, and evaluation of bias and reliability. Explore areas like diagnosis, treatment, and prevention, and distinguish between observational and experimental designs.

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Foundations for Evaluating Clinical Literature: Study Design, Sampling, Bias, and Reliability

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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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