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Activity Number: 325 - Machine Learning Methods for Better-Informed Decision-Making in Heath Care
Type: Invited
Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
Sponsor: Health Policy Statistics Section
Abstract #316997
Title: Predicting the Number of Future Events for Time-to-Event Data
Author(s): Qinglong Tian*
Companies: Iowa State University

This paper describes constructing prediction intervals for the number of future events from a population of units associated with an on-going time-to-event process. Examples include predicting warranty returns and predicting the number of future product failures that could cause serious threats to property or life. Important decisions such as whether a product recall should be mandated are often based on such predictions. Data, generally right-censored (and sometimes left truncated and right-censored), are used to estimate the parameters of a time-to-event distribution. This distribution can then be used to predict the number of events over future periods of time. Such predictions are sometimes called within-sample predictions and differ from other prediction problems considered in most of the prediction literature. This paper shows that the plug-in (also known as estimative or naive) prediction method is not asymptotically correct (i.e., for large amounts of data, the coverage probability always fails to converge to the nominal confidence level). However, a commonly used prediction calibration method is shown to be asymptotically correct for within-sample predictions, and three al

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