The DOI matches its Crossref record; 15 of the 16 references it cites were found in a registry or an index.
Survival Analysis Part II: Multivariate data analysis – an introduction to concepts and methods
Open accessAbstract
Survival analysis involves the consideration of the time between a fixed starting point (e.g. diagnosis of cancer) and a terminating event (e.g. death). The key feature that distinguishes such data from other types is that the event will not necessarily have occurred in all individuals by the time the study ends, and for these patients, their full survival times are unknown. For instance, in studies that measure the length of survival after diagnosis of cancer, it is common for a proportion of individuals to remain alive and disease-free at the end of the follow-up period, and for these patients, we know only a lower limit on their actual time to event. Thus, special methods are required for these type of data. The explanation and demonstration of some of the methods proposed to analyse such data are the basis of this series.
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“The proportional hazards assumption was assessed using the log(−log(survival)) plot, and those that failed the assumption or that were deemed to be time dependent were entered as continuous time-dependent covariates (Bradburn et al, 2003).”
From Cohort study of adherence to adjuvant endocrine therapy, breast cancer recurrence and mortality (2013) · cites this paper as Bradburn et al, 2003
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Citations
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Is the Venture Growth-Survival Relationship Inverted U-Shaped or Not? A Replication and Extension
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“In medical research, for example, it is commonly used to assess patient survival after treatments that begin at different times and vary in duration (Altman & Bland, 1994; Bradburn et al., 2003).”
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“17 Prognostic genes were selected, and a predictive risk score model was developed using LASSO Cox regression analysis via the “ GLMNET ” R package.”
Xie, Z., Sun, Y., Tang, Y., Qi, Q., Liang, J., Zhang, J., Tang, W., & Zhou, X. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27115017
“Kaplan–Meier survival analysis was conducted with log-rank tests to assess group differences [54], and p -values 0.05 were considered statistically significant.”
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Lin, C.-H., Fan, C., Huang, E. P., & Sung, C. (2026) · JMIR Public Health and Surveillance · doi:10.2196/90416
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“(1) Cox Proportional Hazards Model [22] The CPH model was implemented as a traditional method for survival prediction.”
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Liu, C., Zhong, D., Li, J., & Lu, Q. (2026) · BMC Public Health · doi:10.1186/s12889-026-28596-3
“In particular, the Cox proportional hazards model is popular in medical research due to its ability to analyze censored data, its flexibility in not requiring specification of the baseline hazard, and its capacity to handle time-dependent covariates [43].”
Time‑varying EEG survival modeling predicts aviation crash risk in simulated flightPreprint
Yue, X., Feltman, K. A., Vogl, J., & Kelley, A. M. (2026) · Research Square · doi:10.21203/rs.3.rs-10616804/v1
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Registrational Cancer Trial—Design and Analysis Considerations
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Tohmeh, M., Haber, M., Nasr, M., Kanaan, E., Trétarre, B., Foucan, A.-S., & Borie, F. (2026) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2026.112309
Cheng, J., Sun, R., Gong, Y., Fan, D., & Wang, L. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1880227
Metadata
- Authors
- Bradburn, M. J., Clark, T. G., Love, S. B., & Altman, D. G.
- Affiliations
- Cancer Research UK (GB)
- Corresponding author
- Bradburn, M J
- Venue
- British Journal of Cancer
- Published
- 2003-07-29
- Volume
- 89
- Issue
- 3
- Pages
- 431-436
- ISSN
- 0007-0920 (ISSN-L)
- 1532-1827
- PMC
- PMC2394368
- OpenAlex
- W2107482271
- Cited by
- 741
- References
- 16
- Citations by year
- 2026: 31 · 2025: 55 · 2024: 86 · 2023: 67 · 2022: 54 · 2021: 91 · 2020: 55 · 2019: 41 · 2018: 43 · 2017: 35 · 2016: 26 · 2015: 21 · 2014: 27 · 2013: 15 · 2012: 24
- FWCI
- 7.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-sa
- www.springer.com/tdm (unspecified)
- Locations
- British Journal of Cancer (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- Funders
- Cancer Research UK
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Keywords
- survival analysis, multivariate survival data, censored data, time to event, cancer survival, censoring
- MeSH terms
- Clinical Trials as Topic, Humans, Neoplasms, Models, Statistical, Multivariate Analysis, Survival Analysis
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