Evidence map›Paper›PMID 39039022›Full record

ArticleStatistics in medicine2024

An augmented illness-death model for semi-competing risks with clinically immediate terminal events.

Harrison T Reeder, Kyu Ha Lee, Stefania I Papatheodorou, Sebastien Haneuse

Abstract read
In one paragraph

Article in Statistics in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Harrison T ReederBiostatistics, Massachusetts General Hospital, Boston, Massachusetts.ORCID 0000-0001-8813-9127
Kyu Ha LeeDepartment of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Stefania I PapatheodorouDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Sebastien HaneuseDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

Funding

Methods for High-Dimensional Statistical Inference and Individualized Risk Prediction under Semi-Competing RisksF31HD102159 · NICHD · HARVARD SCHOOL OF PUBLIC HEALTH · PI REEDER, HARRISON · 2020 to 2021
$74k
Eunice Kennedy Shriver National Institute of Child Health and Human Development F31HD102159NICHD NIH HHS F31 HD102159
6 · The paper itself

Abstract

Preeclampsia is a pregnancy-associated condition posing risks of both fetal and maternal mortality and morbidity that can only resolve following delivery and removal of the placenta. Because in its typical form preeclampsia can arise before delivery, but not after, these two events exemplify the time-to-event setting of "semi-competing risks" in which a non-terminal event of interest is subject to the occurrence of a terminal event of interest. The semi-competing risks framework presents a valuable opportunity to simultaneously address two clinically meaningful risk modeling tasks: (i) characterizing risk of developing preeclampsia, and (ii) characterizing time to delivery after onset of preeclampsia. However, some people with preeclampsia deliver immediately upon diagnosis, while others are admitted and monitored for an extended period before giving birth, resulting in two distinct trajectories following the non-terminal event, which we call "clinically immediate" and "non-immediate" terminal events. Though such phenomena arise in many clinical contexts, to-date there have not been methods developed to acknowledge the complex dependencies between such outcomes, nor leverage these phenomena to gain new insight into individualized risk. We address this gap by proposing a novel augmented frailty-based illness-death model with a binary submodel to distinguish risk of immediate terminal event following the non-terminal event. The model admits direct dependence of the terminal event on the non-terminal event through flexible regression specification, as well as indirect dependence via a shared frailty term linking each submodel. We develop an efficient Bayesian sampler for estimation and corresponding model fit metrics, and derive formulae for dynamic risk prediction. In an extended example using pregnancy outcome data from an electronic health record, we demonstrate the proposed model's direct applicability to address a broad range of clinical questions.

Indexed as

Models, StatisticalPre-EclampsiaBayes TheoremComputer SimulationFemaleHumansPregnancyRisk AssessmentBayesian survival analysislogistic modelmulti‐state modelrisk predictionsemi‐competing riskstime‐to‐event analysis

Identifiers

PMID39039022
PMCPMC13531691

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.