Event Summary:
A one-day workshop organized by the Center for Interdisciplinary Research and
Consulting at the University of Maryland, Baltimore County (UMBC), and the
Center for Innovative Study Design (CISD) at Stanford University in the field of Clinical Trials and Regulatory Science.
Registration is now closed
⇒View Archives here⇐
Organizing Committee:
Faculty from UMBC and Stanford University and leaders from FDA and industry.
For more information:
Please contact Dr. Yi Huang, Department of Mathematics and
Statistics, University of Maryland Baltimore County (UMBC),
1000 Hilltop Circle, Baltimore, MD 21250.
E-Mail: yihuang@umbc.edu.
For any Technical difficulties Please contact Zana Coulibaly at czana1@umbc.edu.
Participant Information
Joseph Heyse from Merck Research Laboratories.
Paper: Statistical Considerations in Prediction: The Role of “Predicting Observables”
Estimation and hypothesis testing of parameters has been the mainstay of modern statistical inference directed at evaluating medical interventions. However, there is a growing interest on applications of prediction, especially in applications of precision medicine. Basing statistical inference on “observables” offers many advantages in these applications. There is a more direct connection with a specific decision or objective for the analysis; there is a specific rationale and basis for model development; and more meaningful capability for model validation including comparing analytical models. Both Bayesian and non-Bayesian methods are available and can be utilized. This presentation will highlight the growing interest in prediction and describe predicting observables as a useful statistical framework. Two examples will be used to illustrate the main points: (1) an application of health economics for predicting health care costs, and (2) a disease classification problem. The presentation will finish with concluding remarks about the relevance to precision medicine and machine learning.