The DOI matches its Crossref record.
Log-Logistic Regression Models for Survival Data
Abstract
The log‐logistic distribution has a non‐monotonic hazard function which makes it suitable for modelling some sets of cancer survival data. A log‐logistic regression model is described in which the hazard functions for separate samples converge with time. This also provides a linear model for the log odds on survival by any chosen time. The model is fitted on GLIM and an example is given of its use with lung cancer survival data.
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.
Integrity signals
Registry
Cites
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.
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
Cited by
Maximum Likelihood Estimation in the Proportional Odds Model
Murphy, S. A., Rossini, A. J., & van der Vaart, A. W. (1997) · Journal of the American Statistical Association · doi:10.1080/01621459.1997.10474051 · Cited by 276
Mixture models in survival analysis: Are they worth the risk?
Farewell, V. T. (1986) · Canadian Journal of Statistics · doi:10.2307/3314804 · Cited by 271
Time-dependence of hazard ratios for prognostic factors in primary breast cancer
Hilsenbeck, S. G., Ravdin, P. M., de Moor, C. A., Chamness, G. C., Osborne, C. K., & Clark, G. M. (1998) · Breast Cancer Research and Treatment · doi:10.1023/a:1006133418245 · Cited by 237
Metadata
- Authors
- Bennett, S.
- Venue
- Applied Statistics
- Publisher
- JSTOR
- Published
- 1983
- Volume
- 32
- Issue
- 2
- Pages
- 165
- ISSN
- 0035-9254 (ISSN-L)
- OpenAlex
- W2277783454
- Cited by
- 342
- References
- 0
- FWCI
- 6.33
- 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: Log-Logistic Regression Models for Survival Data. https://app.cochat.ai/papers/10.2307/2347295
- The paper
- Bennett, S. (1983). Log-Logistic Regression Models for Survival Data. Applied Statistics. https://doi.org/10.2307/2347295
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.
