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Some Challenges with Input Uncertainty

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Design and Analysis of Experiments

In this short presentation, I will summarize several recent applications from various parts of health care that highlight several interesting challenges with modeling and input uncertainty. Beyond the usual challenges associated with Bayesian model average-type approaches for parameter uncertainty about statistical input parameters, some challenges will also be identified. They include the difficulty that some decision makers have in thinking about data for input parameters when output data is easier to observe, data on intermediate or surrogate endpoints may be easier or less expensive to collect, and the structure of the model that translates inputs to outputs itself might be uncertain. These examples and challenges have a number of implications, many not yet adequately solved, for policy decisions, the sensitivity of decisions to input uncertainty, the prioritization of data to reduce input uncertainty in a way that effectively, when to stop learnin g and when to make a system design decision, and how to model extreme events (e.g., heavy tails) that may have implications for the nonexistence of certain moments of interest. We will review a number of these as time permits.

This talk is part of the Isaac Newton Institute Seminar Series series.

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