Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient-Specific Survival Probabilities in Clinical Trial Data.

Abstract readComparative Study

In one paragraph

Article in Biometrical journal. Biometrische Zeitschrift, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Handling missing modalities in multimodal survival prediction for non-small cell lung cancer.NPJ digital medicine · 2026
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.

Ricarda GrafInstitute of Mathematics, University of Augsburg, Augsburg, Germany.ORCID https://orcid.org/0000-0002-0149-479X

Susan ToddDepartment of Mathematics and Statistics, University of Reading, Reading, UK.ORCID https://orcid.org/0000-0002-9981-923X

M Fazil BakshDepartment of Mathematics and Statistics, University of Reading, Reading, UK.ORCID https://orcid.org/0000-0003-3107-8815

Funding

International Dimension of ERASMUS+Young Researchers Travel Scholarship Program of the University of Augsburg

6 · The paper itself

Abstract

The Cox proportional hazards model is often used to analyze data from randomized controlled trials (RCTs) with time-to-event outcomes. Random survival forest (RSF) is a machine-learning algorithm known for its high predictive performance. We conduct a comprehensive neutral comparison study to compare the performance of Cox regression and RSF in various simulation scenarios based on two reference datasets from RCTs. The motivation is to identify settings in which one method is preferable over the other when comparing different aspects of performance using measures according to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis) recommendations. Our results show that conclusions solely based on the

Indexed as

BiometryRandomized Controlled Trials as TopicHumansPrediction AlgorithmsProportional Hazards ModelsRandom ForestSurvival AnalysisCox regressionRandomized controlled trialsRandom survival forestSimulation study

Identifiers

PMID42798169

DOI10.1002/bimj.70171

PMCPMC13615326

What OpenQuestion holds

Textmetadata

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.