To help meet METRICS’ goal of establishing meta-research as a new field of study and to improve how researchers conduct, report and disseminate their findings, we support a variety of education and training opportunities. Through the development of curricula to be delivered in the classroom and online, METRICS intends to produce a new generation of scholars focused on transforming research practices and improving the quality of scientific studies.
If you have relevant educational resources you would like to share, please email John Ioannidis or Steven Goodman..
Essentials of Clinical Research at Stanford
Taught by METRICS Co-director, Professor Steven Goodman, this 11 session course will cover design and analysis of clinical studies, good clinical practices, data management, and regulatory guidance, and conducting ethical research and research reproducibility. Students may also register for credit.
HRP 206: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (MED 206, STATS 211)
Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Meta-analysis as a quantitative (statistical) method for combining results of independent studies. Examples from medicine, epidemiology, genomics, ecology, social/behavioral sciences, education. Collaborative analyses. Project involving generation of a meta-research project or reworking and evaluation of an existing published meta-analysis. Prerequisite: knowledge of basic statistics. Terms: Win | Units: 3 | Grading: Medical Satisfactory/No Credit
HRP219: 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.
Improving your Statistical Inferences
Eindhoven University of Technology This course aims to help participants draw better statistical inferences from empirical research. This course is designed for 3rd year bachelor students, master students, or starting PhD students with some basic knowledge of statistical tests.
Introduction to the Principles and Practice of Clinical Research (IPPCR)
NIH Clinical Center A course to train participants on how to effectively conduct clinical research. The course will cover the spectrum of clinical research and the research process by highlighting epidemiologic methods, study design, protocol preparation, patient monitoring, quality assurance, and Food and Drug Administration (FDA) issues. Intended for physicians, basic scientists, medical students, nurses, and any other health professional seeking to enhance their knowledge in clinical research.
John Hopkins Bloomberg School of Public Health A course dedicated to the concepts and tools behind reporting modern data analyses in a reproducible manner. Participants will be introduced to literate statistical analysis tools, which allow publishing of data analyses in a single document for easy replication of the same analysis to obtain the same results. Part of the Data Science Specialization series offered via Coursera and is intended for those with a background and/or interest in data science.