Evidence map›Paper›PMID 26441487›Full record

ArticleStatistical modelling2015

Cox Regression Models with Functional Covariates for Survival Data.

Jonathan E Gellar, Elizabeth Colantuoni, Dale M Needham, Ciprian M Crainiceanu

Abstract read
In one paragraph

Article in Statistical modelling, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.Journal of the American Statistical Association · 2026
    Article
  6. Article
  7. Quantile index predictors using R package hyper.gam.Bioinformatics (Oxford, England) · 2025
    Article
  8. Article
  9. Article
  10. Quantile Index Biomarkers Based on Single-Cell Expression Data.Laboratory investigation; a journal of technical methods and pathology · 2023
    Article
  11. Article
  12. Article
  13. Additive Functional Cox Model.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2021
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

4 authors.

Jonathan E GellarDepartment of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Elizabeth ColantuoniDepartment of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Dale M NeedhamPulmonary & Critical Care Medicine, and Physical Medicine & Rehabilitation, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Ciprian M CrainiceanuDepartment of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.

Funding

Statistical Methods for Multilevel Multivariate Functional StudiesR01NS060910 · NINDS · JOHNS HOPKINS UNIVERSITY · PI Ciprian M Crainiceanu · 2009 to 2026
$8.3M
Pre-Doctoral Training in Environmental BiostatisticsT32ES012871 · NIEHS · JOHNS HOPKINS UNIVERSITY · PI PENG, ROGER · 2004 to 2014
$1.9M
Statistical methods for biosignals with varying domainsR01HL123407 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI CRAINICEANU, CIPRIAN M · 2014 to 2017
$1.6M
NHLBI NIH HHS R01 HL123407NIEHS NIH HHS T32 ES012871NINDS NIH HHS R01 NS060910
6 · The paper itself

Abstract

We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally-spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge and daily measures of disease severity collected in the intensive care unit, among survivors of acute respiratory distress syndrome.

Indexed as

Cox proportional hazards modelfunctional data analysisintensive care unitnonparametric statisticssurvival analysis

Identifiers

PMID26441487
PMCPMC4591554

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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.