Foundations for Evaluating Clinical Literature. Outline Definitions Study Design Sampling Bias Reliability and validity.

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Presentation transcript:

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) –Typically animal models –Human and animal cell cultures and biologic samples –Genetic material Translational Research –New area (relatively)

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

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

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

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

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

Measurement Two types of issues to expect: –Random (instrument sensitivity) –Systematic (instrument bias)

Reliability vs. Variability Observer Instrument Subject

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

Validity/Accuracy PresentAbsent Positivea (TP)b (FP) Negativec (FN)d (TN) Outcome condition (disease) Test Sensitivity = TP/(TP+FN) Specificity = TN/(FP+TN) PPV = TP/(TP+FP) NPV = TN/(TN+FN)

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)