1 / 13

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)

jreuter
Télécharger la présentation

Foundations for Evaluating Clinical Literature

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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)

More Related