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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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“Cox regression is used to analyze survival models with continuous and categorical predictor variables (Bradburn et al., 2003).”
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Operationalizing Environmental Sustainability through Policy-based and Market-based Approaches
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Modelling empirical features and liquidity resilience in the Limit Order Book
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Estimation, calibration, and validation
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“Both these figures (5 and 6) clearly show a straight line which validates that log-logistic form is best fitted (Bradburn et al., 2003; Kleinbaum & Klein, 2005, p. 279).”
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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
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.
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