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Activity Number: 359 - Contributed Poster Presentations: Biopharmacutical Section
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #329448
Title: Univariate, Multivariate and Model-Based Prediction on Truncated Continuous Data with Shiny/R
Author(s): Qianqiu Li*
Companies: Janssen Research & Development
Keywords: Truncation; Shiny; bootstrap; Bayesian; predictive intervals

Truncation occurs when a value beyond a threshold is not detectable or measurable. In analysis of non-negligible continuous data with either one-sided truncation or interval truncation, this work aims at the prediction problems under univariate and multivariate distributions and via linear fixed and mixed effects models. Using bootstrap and Bayesian approaches integrated in a Shiny application, the percentiles and predictive intervals given defined coverage probabilities can be calculated for a specified population. In addition, the applicability of the prediction work is demonstrated with simulation data in justification of specification and comparability studies.

Authors who are presenting talks have a * after their name.

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