Evaluating Technologies for Diagnosis, Prediction and Screening


Stanford School of Engineering

Health & Medicine: 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 an understanding of introductory biostatistics, epidemiologic and clinical research study design.

NOTE: This course updated its course number, please enroll under the new course number EPI219.

What you will learn

How to evaluate the value of tests aimed at diagnosing disease, predicting disease or treatment response, or to screen for disease. The course will show how these principles underlie health technology assessment, comparative effectiveness research and the emerging field of personalized medicine.

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


Intended audience: 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.

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.

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