The DOI matches its Crossref record.
Comparability of Segmented Line Regression Models
Abstract
Summary Segmented line regression models, which are composed of continuous linear phases, have been applied to describe changes in rate trend patterns. In this article, we propose a procedure to compare two segmented line regression functions, specifically to test (i) whether the two segmented line regression functions are identical or (ii) whether the two mean functions are parallel allowing different intercepts. A general form of the test statistic is described and then the permutation procedure is proposed to estimate the p‐value of the test. The permutation test is compared to an approximate F‐test in terms of the p‐value estimation and the performance of the permutation test is studied via simulations. The tests are applied to compare female lung cancer mortality rates between two registry areas and also to compare female breast cancer mortality rates between two states.
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
Estimating average annual per cent change in trend analysis
Clegg, L. X., Hankey, B. F., Tiwari, R., Feuer, E. J., & Edwards, B. K. (2009) · Statistics in Medicine · doi:10.1002/sim.3733 · Cited by 1,256
Self-citationXiao, Y., Cerel, J., & Mann, J. J. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.13513 · Cited by 166
Olsen, C. M., Pandeya, N., Neale, R. E., & Whiteman, D. C. (2024) · Medical Journal of Australia · doi:10.5694/mja2.52293 · Cited by 3
Metadata
- Authors
- Kim, H., Fay, M. P., Yu, B., Barrett, M. J., & Feuer, E. J.
- Venue
- Biometrics
- Publisher
- Oxford University Press (OUP)
- Published
- 2004-12
- Volume
- 60
- Issue
- 4
- Pages
- 1005-1014
- ISSN
- 0006-341X (ISSN-L)
- 1541-0420
- OpenAlex
- W2130160532
- Cited by
- 366
- References
- 0
- FWCI
- 0.98
- 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: Comparability of Segmented Line Regression Models. https://app.cochat.ai/papers/10.1111/j.0006-341x.2004.00256.x
- The paper
- Kim, H., Fay, M. P., Yu, B., Barrett, M. J., & Feuer, E. J. (2004). Comparability of Segmented Line Regression Models. Biometrics. https://doi.org/10.1111/j.0006-341x.2004.00256.x
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.
