Evidence map›Paper›PMID 27034534›Full record

ArticleScandinavian journal of statistics, theory and applications2016

A Proportional Hazards Regression Model for the Sub-distribution with Covariates Adjusted Censoring Weight for Competing Risks Data.

Peng He, Frank Eriksson, Thomas H Scheike, Mei-Jie Zhang

Abstract read
In one paragraph

Article in Scandinavian journal of statistics, theory and applications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

  1. Trial
  2. Article
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  4. Article
  5. Article
  6. Article
  7. Article
  8. Adjusted curves for clustered survival and competing risks data.Communications in statistics: Simulation and computation · 2025
    Article
  9. Article
  10. Article
  11. Article
  12. Observational
  13. Article
  14. Article
  15. Accelerated and Interpretable Oblique Random Survival Forests.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024
    Article
  16. Article
  17. Article
  18. Article
  19. Review
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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.

Peng HeDivision of Biostatistics, Medical College of Wisconsin, U.S.A.
Frank ErikssonDepartment of Biostatistics, University of Copenhagen, Denmark.
Thomas H ScheikeDepartment of Biostatistics, University of Copenhagen, Denmark.
Mei-Jie ZhangDivision of Biostatistics, Medical College of Wisconsin, U.S.A.

Funding

Data Resource for Analyzing Blood &Marrow TransplantsU24CA076518 · NCI · MEDICAL COLLEGE OF WISCONSIN · PI Amy M Moskop, Bronwen Shaw · 1998 to 2026
$105.2M
NCI NIH HHS U24 CA076518
6 · The paper itself

Abstract

With competing risks data, one often needs to assess the treatment and covariate effects on the cumulative incidence function. Fine and Gray proposed a proportional hazards regression model for the subdistribution of a competing risk with the assumption that the censoring distribution and the covariates are independent. Covariate-dependent censoring sometimes occurs in medical studies. In this paper, we study the proportional hazards regression model for the subdistribution of a competing risk with proper adjustments for covariate-dependent censoring. We consider a covariate-adjusted weight function by fitting the Cox model for the censoring distribution and using the predictive probability for each individual. Our simulation study shows that the covariate-adjusted weight estimator is basically unbiased when the censoring time depends on the covariates, and the covariate-adjusted weight approach works well for the variance estimator as well. We illustrate our methods with bone marrow transplant data from the Center for International Blood and Marrow Transplant Research (CIBMTR). Here cancer relapse and death in complete remission are two competing risks.

Indexed as

competing riskscumulative incidence functioninverse probability of censoring weightproportional hazards modelsubdistribution

Identifiers

PMID27034534
PMCPMC4809648

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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.