Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 656

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Semiparametric analysis of the additive risk model

LIN, D. Y., YING, Z.·1994·Biometrika
DOI 10.1093/biomet/81.1.61
Open this paper in CoChat — free account

Abstract

In contrast to the proportional hazards model, the additive risk model specifies that the hazard function associated with a set of possibly time-varying covariates is the sum of, rather than the product of, the baseline hazard function and the regression function of covariates. This formulation describes a different aspect of the association between covariates and the failure time than the proportional hazards model, and is more plausible than the latter for many applications. In the present paper, simple procedures with high efficiencies are developed for making inference about the regression parameters under the additive risk model with an unspecified baseline hazard function. The subject-specific survival estimation is also studied. The proposed techniques resemble the partial-likelihood-based methods for the proportional hazards model. A real example is provided.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/biomet/81.1.61

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 9 references

Cited by

See all 656

Metadata

Authors
LIN, D. Y., & YING, Z.
Venue
Biometrika
Publisher
Oxford University Press (OUP)
Published
1994-03-01
Volume
81
Issue
1
Pages
61-71
ISSN
  • 0006-3444 (ISSN-L)
  • 1464-3510
DOI
10.1093/biomet/81.1.61
OpenAlex
W2052464909
Cited by
656
References
9
FWCI
3.74
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Semiparametric analysis of the additive risk model. https://app.cochat.ai/papers/10.1093/biomet/81.1.61
The paper
LIN, D. Y., & YING, Z. (1994). Semiparametric analysis of the additive risk model. Biometrika. https://doi.org/10.1093/biomet/81.1.61

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.