A Support vector machine-based mixture cure model for mixed case interval censored data.

Suvra Pal, Wisdom Aselisewine

Abstract read

In one paragraph

Article in Statistics and computing, 2026. 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

2 authors.

Suvra PalDepartment of Mathematics, University of Texas at Arlington, Arlington, Texas 76019 USA.

Wisdom AselisewineDepartment of Mathematics, University of Texas at Arlington, Arlington, Texas 76019 USA.

Funding

Machine Learning-Based Predictive Models for Disease Cure and Computationally Efficient Methods in High-Dimensional SettingsR35GM156859 · NIGMS · UNIVERSITY OF TEXAS ARLINGTON · PI SUVRA PAL · 2025 to 2026

$643k

Using Machine Learning to Improve the Predictive Accuracy of Disease CureR15GM150091 · NIGMS · UNIVERSITY OF TEXAS ARLINGTON · PI PAL, SUVRA · 2023 to 2023

$452k

NIGMS NIH HHS R15 GM150091NIGMS NIH HHS R35 GM156859

6 · The paper itself

Abstract

We propose a semi-parametric two-component model for the analysis of mixed case interval censored (MCIC) data with a cured subgroup. Such data occurs when the time to an event of interest is only known to belong to an interval obtained from a sequence of, say, Supplementary Information: The online version contains supplementary material available at 10.1007/s11222-025-10796-3.

Indexed as

Cure RateEM AlgorithmMachine learningPlatt ScalingPredictive Accuracy

Identifiers

PMID41552415

DOI10.1007/s11222-025-10796-3

PMCPMC12811344

What OpenQuestion holds

Textmetadata

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.