Evidence map›Paper›PMID 31436136›Full record

ArticleStatistical methods in medical research2020

Time-to-event analysis when the event is defined on a finite time interval.

Catherine Lee, Stephanie J Lee, Sebastien Haneuse

Abstract read
In one paragraph

Article in Statistical methods in medical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 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

3 authors.

Catherine LeeDivision of Research, Kaiser Permanente, Oakland, CA, USA.ORCID 0000-0003-0008-9052
Stephanie J LeeClinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.ORCID 0000-0003-2600-6390
Sebastien HaneuseDepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.ORCID 0000-0003-4963-0655

Funding

Data Resource for Analyzing Blood &Marrow TransplantsU24CA076518 · NCI · MEDICAL COLLEGE OF WISCONSIN · PI Amy M Moskop, Bronwen Shaw · 1998 to 2026
$105.2M
Clustered semi-competing risks analysis in quality of end-of-life care studiesR01CA181360 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANEUSE, SEBASTIEN · 2014 to 2017
$1.8M
NCI NIH HHS R01 CA181360NCI NIH HHS U24 CA076518
6 · The paper itself

Abstract

Acute graft-versus-host disease (GVHD) is a frequent complication following hematopoietic cell transplantation (HCT). Research on risk factors for acute GVHD has tended to ignore two important clinical issues. First, post-transplant mortality is high. In our motivating data, 100-day post-HCT mortality was 15.4%. Second, acute GVHD in its classic form is only diagnosed within 100 days of the transplant; beyond 100 days, a patient may be diagnosed with late onset acute or chronic GVHD. Standard modeling of time-to-event outcomes, however, generally conceive of patients being able to experience the event at any point on the time scale. In this paper, we propose a novel multi-state model that simultaneously: (i) accounts for mortality through joint modeling of acute GVHD and death, and (ii) explicitly acknowledges the finite time interval during which the event of interest can take place. The observed data likelihood is derived, with estimation and inference via maximum likelihood. Additionally, we provide methods for estimating the absolute risk of acute GVHD and death simultaneously. The proposed framework is compared via comprehensive simulations to a number of alternative approaches that each acknowledge some but not all aspects of acute GVHD, and illustrated with an analysis of HCT data that motivated this work.

Indexed as

Graft vs Host DiseaseHematopoietic Stem Cell TransplantationHumansAcute graft-versus-host diseasecure modelssemi-competing risks

Identifiers

PMID31436136
PMCPMC8096611

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Registered trials

None linked

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.