Evaluating Technologies for Diagnosis, Prediction and Screening

New technologies designed to monitor and improve health outcomes are constantly emerging, but most fail in the clinic and in the marketplace because relatively few are supported by reliable, reproducible evidence that they produce a health benefit. This course covers the designs and methods that should be used to evaluate technologies to diagnose patients, predict prognosis or other health events, or screen for disease. These technologies can include devices, statistical prediction rules, biomarkers, gene panels, algorithms, imaging, or any information used to predict a future or a previously unknown health state.

Specific topics to be covered include the phases of test development, how to frame a proper evaluation question, measures of test accuracy, Bayes theorem, internal and external validation, prediction evaluation criteria, decision analysis, net-utility, ROC curves, c-statistics, net reclassification index, decision curves and reporting standards. Examples of technology assessments and original methods papers are used. Knowledge of statistical software is not required, although facility with at least Excel for basic calculations is needed.

Open to students with a solid grounding in epidemiologic principles, clinical study design and some knowledge of clinical medicine, interested in the role of testing in clinical and preventive medicine. This course was formerly numbered HRP219

Topics Include

  • Mathematics of diagnostic testing
  • Design and reporting of diagnostic test studies: Phases and purposes of evaluation
  • Common biases in diagnostic test studies
  • Decision analysis: Measuring utility, drawing decision trees, applied to diagnostic tests
  • Choosing optimal test cutoffs, Expected value of diagnostic information
  • Development and assessment of prediction/prognostic models
  • Design and interpretation of screening studies
  • Common biases in screening studies

Course Page
Price
$4,056.00 Subject to change
Delivery
Online, instructor-led
Level
Advanced
Commitment
10 weeks, 5-15 hrs/week
Credit
Epidemiology and Clinical Research Graduate Certificate
School
Stanford School of Medicine
Language
English