Evidence map›Paper›PMID 35318383›Full record

ArticleScientific reports2022

A hierarchical Bayesian entry time realignment method to study the long-term natural history of diseases.

Liangbo L Shen, Lucian V Del Priore, Joshua L Warren

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Trial
  2. Article
  3. Long-term natural history of ellipsoid zone width inThe British journal of ophthalmology · 2025
    Article
  4. Article
  5. Article
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.

Liangbo L ShenDepartment of Ophthalmology, University of California San Francisco, San Francisco, CA, USA.
Lucian V Del PrioreDepartment of Ophthalmology and Visual Science, Yale University School of Medicine, 40 Temple Street, Suite 1B, New Haven, CT, 06510, USA. lucian.delpriore@yale.edu.
Joshua L WarrenDepartment of Biostatistics, Yale School of Public Health, 350 George Street, New Haven, CT, 06511, USA. joshua.warren@yale.edu.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

A major question in clinical science is how to study the natural course of a chronic disease from inception to end, which is challenging because it is impractical to follow patients over decades. Here, we developed BETR (Bayesian entry time realignment), a hierarchical Bayesian method for investigating the long-term natural history of diseases using data from patients followed over short durations. A simulation study shows that BETR outperforms an existing method that ignores patient-level variation in progression rates. BETR, when combined with a common Bayesian model comparison tool, can identify the correct disease progression function nearly 100% of the time, with high accuracy in estimating the individual disease durations and progression rates. Application of BETR in patients with geographic atrophy, a disease with a known natural history model, shows that it can identify the correct disease progression model. Applying BETR in patients with Huntington's disease demonstrates that the progression of motor symptoms follows a second order function over approximately 20 years.

Indexed as

Huntington DiseaseBayes TheoremComputer SimulationDisease ProgressionHumansResearch Design

Identifiers

PMID35318383
PMCPMC8941125

What OpenQuestion holds

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Registered trials

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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.