Evidence map›Paper›PMID 40727041›Full record

ArticleJournal of statistical theory and practice2025

A Weighted Survival Regression Framework for Incorporating External Prediction Information.

Debashis Ghosh

Abstract read
In one paragraph

Article in Journal of statistical theory and practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Debashis GhoshDepartment of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045 USA.ORCID 0000-0001-6618-1316

Funding

Statistical Methods for Cancer BiomarkersR01CA129102 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GHOSH, DEBASHIS, TAYLOR, JEREMY M.G. · 2009 to 2021
$3.3M
NCI NIH HHS R01 CA129102
6 · The paper itself

Abstract

In this article, we develop a weighted approach to estimation for right-censored time to event data in the presence of external predictions available from a prediction model. There are several advantages to the proposed approach. First, the method allows for arbitrary forms for the external prediction model. Second, the methodology can be fit easily using standard software packages that allow for subject-specific weights. Third, all that is needed from the external models are access to predictions and not the actually prediction equation. A complication is that inference becomes challenging, so we develop new theoretical results along with a perturbation-based method for inference. The methodology is applied to three publicly available datasets.

Indexed as

Additive hazardsProportional hazardsRisk predictionSemiparametric regressionSurvival analysis

Identifiers

PMID40727041
PMCPMC12296798

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.