Bayesian Stacked Parametric Survival with Frailty Components and Interval-Censored Failure Times: An Application to Food Allergy Risk

Matthew W. Wheeler, Joost Westerhout, Joe L. Baumert, Benjamin C. Remington

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

To better understand the risk of exposure to food allergens, food challenge studies are designed to slowly increase the dose of an allergen delivered to allergic individuals until an objective reaction occurs. These dose-to-failure studies are used to determine acceptable intake levels and are analyzed using parametric failure time models. Though these models can provide estimates of the survival curve and risk, their parametric form may misrepresent the survival function for doses of interest. Different models that describe the data similarly may produce different dose-to-failure estimates. Motivated by predictive inference, we developed a Bayesian approach to combine survival estimates based on posterior predictive stacking, where the weights are formed to maximize posterior predictive accuracy. The approach defines a model space that is much larger than traditional parametric failure time modeling approaches. In our case, we use the approach to include random effects accounting for frailty components. The methodology is investigated in simulation, and is used to estimate allergic population eliciting doses for multiple food allergens.

Original languageEnglish (US)
Pages (from-to)56-66
Number of pages11
JournalRisk Analysis
Volume41
Issue number1
DOIs
StatePublished - Jan 2021

Keywords

  • Accelerated failure time models
  • ensemble learning
  • model averaging
  • random effects

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Physiology (medical)

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