Some Challenges with Input Uncertainty

29 mins 2 secs, 120.94 MB, Flash Video 484x272, 29.97 fps, 44100 Hz, 568.75 kbits/sec

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Description:	Chick, S (INSEAD) Thursday 08 September 2011, 10:00-10:30

Created:

2011-09-14 15:24

Collection:

Design and Analysis of Experiments

Publisher:

Isaac Newton Institute

Chick, S

Language:

eng (English)

Credits:

Author:	Chick, S
Director:	Steve Greenham


Abstract:	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.

Abstract:

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.

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