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.
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
Figures
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
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
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
Miller, R. G. (1981) · Cited by 3,336
Modelling for Survival Data in Medical Research
(1995) · Technometrics · doi:10.1080/00401706.1995.10485920 · Cited by 2,159
Modelling Survival Data in Medical Research
Collett, D. (1994) · Springer eBooks · doi:10.1007/978-1-4899-3115-3 · Cited by 1,064
Survival Analysis Part I: Basic concepts and first analyses
Clark, T. G., Bradburn, M. J., Love, S., & Altman, D. G. (2003) · British Journal of Cancer · doi:10.1038/sj.bjc.6601118 · Cited by 1,045 · cited 11×
“Examples of survival and hazard functions derived from some of these parametric models were presented in the previous paper (Clark et al , 2003).”
InfluentialSelf-citationLinear regression with censored data
Buckley, J. D., & JAMES, I. R. (1979) · Biometrika · doi:10.1093/biomet/66.3.429 · Cited by 1,032 · cited 1×
“Alternative methods include the method of Buckley and James (1979), which is discussed by Stare et al (2000), and semiparametric AFT models, in which the baseline survivor function is estimated nonparame-trically (see Wei, 1992, for an overview), but have not yet been widely implemented in…”
Modelling Survival Data in Medical Research.
McLachlan, G. J., & Collett, D. (1994) · Biometrics · doi:10.2307/2533471 · Cited by 1,023
Wei, L. J. (1992) · Statistics in Medicine · doi:10.1002/sim.4780111409 · Cited by 827 · cited 1×
“…include the method of Buckley and James (1979), which is discussed by Stare et al (2000), and semiparametric AFT models, in which the baseline survivor function is estimated nonparame-trically (see Wei, 1992, for an overview), but have not yet been widely implemented in statistical software.”
A linear regression model for the analysis of life times
Aalen, O. O. (1989) · Statistics in Medicine · doi:10.1002/sim.4780080803 · Cited by 725 · cited 1×
“These important issues will be addressed in the subsequent paper in this series.”
Multivariate survival analysis using Cox's regression model
Christensen, E. I. (1987) · Hepatology · doi:10.1002/hep.1840070628 · Cited by 372 · cited 1×
“This information could then be displayed via tabular or Christensen (1987) based on the Cox model, but can also be used when fitting fully parametric models.”
Cited by
Sorted by citations
Items 601–700 of 738
Changing concepts and trends in rectal cancer patients
Jalil, O. (2017) · Anglia Ruskin Research Online (Anglia Ruskin University)
A Tentative Staging of Multiple Myeloma by Utilizing Respective Coefficients of Prognostic Factors
Jalaeikhoo, H., Sharifzadeh, M., & Rajaeinejad, M. (2018) · doi:10.30699/acadpub.mci.2.1.22
“[5], one simple and possibly underused quantity for a given risk group is gamma value that is derived from the proportional Abstract Introduction: Multiple myeloma is a heterogeneous disease with different survival times among patients.”
BackgroundMethodPredicting the Risk of Recurrence in Retroperitoneal Sarcoma
Callegaro, D., Gronchi, A., Napolitano, A., & Vincenzi, B. (2018) · Updates in surgery/Updates in surgery series · doi:10.1007/978-88-470-3980-3_12
Pallás, J. M. A. A., & Villa, J. J. (2004) · Elsevier eBooks · doi:10.1016/b978-84-8174-709-6.50027-0
Carrigan, C. E., & Ray, M. H. (2019) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/0361198118821313
“‘‘While the log-rank test provides a p-value for the differences between groups, it offers no estimate of the actual effect size; in other words, it offers a statistical, but not a clinical, assessment of the factor’s impact’’ (18).”
BackgroundMethodVariables Influencing the Association
ter Wee, M. M., & Lissenberg-Witte, B. I. (2019) · Bohn Stafleu van Loghum eBooks · doi:10.1007/978-90-368-2248-0_6
ter Wee, M. M., & Lissenberg-Witte, B. I. (2019) · Bohn Stafleu van Loghum eBooks · doi:10.1007/978-90-368-2248-0_7
Developing statistical and bioinformatic analysis of genomic data from tumours
Thakur, R. (2018) · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)
Wide and Deep Learning for Peer-to-Peer LendingPreprint
Bastani, K., Asgari, E., & Namavari, H. (2018) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1810.03466
Wen, J., Chen, J., & Fan, M. (2019) · Lung Cancer · doi:10.1016/j.lungcan.2019.07.026
Quantitative analysis of tumor heterogeneity based on magnetic resonance images
Trajkovska, E. (2019) · NORA - Norwegian Open Research Archives
Sandanaraj, E. (2019) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/136519
Investigating long-term outcomes and cure for children and young people diagnosed with cancer
Smith, L. (2019) · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York)
Smith, E., Reimer, D., & Kjeldsen, C. C. (2020) · Dansk Universitetspædagogisk Tidsskrift · doi:10.7146/dut.v15i28.115942
Social Queues (Cues)Preprint
Ülkü, S., Hydock, C., & Cui, S. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3428687
Modeling and Analysis of Survival to Patients of Pancreatic Cancer in Krasnoyarsk Territory
Kazarin, A. S., Lukyanova, N. A., & Melnikova, O. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-51974-2_56
Prognostic Survival Factors in Acute Liver Failure Patients in India
George, N., Jose, A., Dharamsi, S., Leelakrishnan, V., & Mathew, A. (2020) · Advanced Biomedical Research · doi:10.4103/abr.abr_70_19
Sennfält, S. (2020) · Lund University Publications (Lund University)
Length of stay and palliative care:Understanding long-term care facility residents in Europe
Moore, D. C. (2021) · Lancaster EPrints (Lancaster University)
Bekiroğlu, N., & Ülgen, A. (2021) · Pamukkale Medical Journal · doi:10.31362/patd.893954
Fitt, B., Loy, G., Christopher, E., Brennan, P. M., & Poon, M. T. C. (2021) · medRxiv · doi:10.1101/2021.09.04.21263119
“Univariable survival analyses that take on only one molecular marker do not account for other markers or clinical features.(8) The resulting associations from such analyses are subjected to confounding effects, which may render them unreliable.”
BackgroundThe impact of the progranulin-sortilin axis on breast cancer stem cell activity and patient outcome
Berger, K. (2021) · Gothenburg University Publications Electronic Archive (Gothenburg University)
Part II : Survival analysis and common applications
Touchette, D. R., & Rodriguez, R. W. (2021) · JACCP JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY · doi:10.1002/jac5.1562
Malkova, A. M., Орлова, Р. В., Zhukova, N. V., Gubal, A. R., & Sharoiko, V. V. (2022) · Siberian Journal of Oncology · doi:10.21294/1814-4861-2022-21-2-109-117
PhD, R. B. H. M. M., & Ron Goeree, M. A. (2015) · CRC Press eBooks · doi:10.1201/b18285-7
Schmoor, C., & Graf, E. (2014) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat06949
Prognostic significance of SDF-1 in colorectal cancer depends on CD8+ T-cell densityPreprint
Lalos, A., Tülek, A., Tosti, N., Mechera, R., Wilhelm, A., Soysal, S. D., Däster, S., Kancherla, V., Weixler, B., Spagnoli, G. C., et al. (2020) · doi:10.21203/rs.2.23094/v1
“Multivariate analyses showing Hazard Ratios and p-value for all CRCs (n = 576 less than 613 due to missing values) conferred by SDF-1 expression and CD8 + cell density, age, sex, tumor size, lymph node involvement, tumor grade, vascular invasion, tumor border configuration and microsatellite stability [33].”
MethodSchmoor, C., & Graf, E. (2006) · Wiley Encyclopedia of Clinical Trials · doi:10.1002/9780471462422.eoct039
Schmoor, C., & Graf, E. (2005) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess7176
Huang, Q. Y., Lyv, J., Li, B., Ma, L., Deng, T., & Zeng, X.-T. (2020) · doi:10.21203/rs.2.18724/v2
“Finally, univariable and multivariable regressions are used to investigate the association between participants’ survival time and one or more factors[2].”
MethodSphingolipids: A Roadmap from Biomarker Identification to Clinical Application
Bhadwal, P., & Agnihotri, N. (2022) · Springer eBooks · doi:10.1007/978-981-16-4345-3_27
Nicolas, R., Rivas-Ruíz, R., Reyes‐López, A., & Ureña-Wong, K. (2022) · medRxiv · doi:10.1101/2022.09.29.22280276
The capacity for adaptation to climate warming in an annual plant ( Brassica rapa )Preprint
So, C. P., Grieshop, K., & Weis, A. E. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.01.510426
Kalinkin, A. I., Sigin, V. O., Nemtsova, M. V., & Стрельников, В. В. (2022) · Вестник Российского государственного медицинского университета · doi:10.24075/vrgmu.2022.056
Pan, J., Gu, X., Luo, J., Qian, X., Gao, Q., Li, T., Ye, L., & Li, C. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.1073688
“To remove the multicollinearity among parameters, we also performed the Least absolute shrinkage and selection operator (LASSO) regression through the “glmnet” package (Engebretsen and Bohlin, 2019) and signatures with prominent prognostic values were further included into multivariate Cox regression (stepwise model)…”
MethodThe Role of IPO Proceeds on Survival of Companies: A Survival Analysis ApproachPreprint
Alyasa-Gan, S. S., Yahya, N. C., & Low, R. K. Y. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4126777
Correlates of time to first citation in ecology and taxonomyPreprint
Guedes, J. J. M., Melo, I., Barbosa, I. L. B. D., & Nunes, M. L. S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.16.532892
“The time to first citation was investigated through time-to-event analysis, also known as survival analysis (Bradburn et al. 2003; Clark et al. 2003).”
MethodUse of Survival Data in Multivariate Adaptive Regression Analysis: Simulation Study
Şengül, M. T., Temel, G. Ö., & Kaya, İ. E. (2023) · Turkiye Klinikleri Journal of Biostatistics · doi:10.5336/biostatic.2022-94594
Lischka, A., Doberstein, N., Freitag‐Wolf, S., Koçak, A. A., Gemoll, T., Heselmeyer‐Haddad, K., Ried, T., Auer, G., & Habermann, J. K. (2023) · doi:10.1158/1078-0432.c.6529475.v1
Lischka, A., Doberstein, N., Freitag‐Wolf, S., Koçak, A. A., Gemoll, T., Heselmeyer‐Haddad, K., Ried, T., Auer, G., & Habermann, J. K. (2023) · doi:10.1158/1078-0432.c.6529475
Flores-Trejo, O. L., Buendía-Espinoza, J. C., & Zamora-Elizalde, M. C. (2023) · European Journal of Forest Research · doi:10.1007/s10342-023-01570-z
“Cox proportional hazards regression model (Bradburn et al. 2003; Cox 1972) predicts a species' death probability at a given time”
MethodDoes Time Frame of IPO Proceeds Predict Survival of Firms? Evidence from the Malaysian Market
Alyasa-Gan, S. S., Che-Yahya, N., & Low, R. K. Y. (2023) · Journal of Emerging Market Finance · doi:10.1177/09726527231178086
“Typically, the survival time is translated to a natural logarithm and specified as a loglinear function (Bradburn et al., 2003), specified as follows:”
BackgroundPrivacy Preserving Machine LearningPreprint
Meier, D., & Troncoso-Pastoriza, J. R. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4595287
From foundations to frontiers: Navigating survival analysis in the era of big data and deep learning
Zhang, J. (2023) · Theoretical and Natural Science · doi:10.54254/2753-8818/9/20240706
Follow-up durch flächendeckende Krebsregister in Deutschland: wertvolle Daten auf solider Grundlage
Holleczek, B., Nennecke, A., Fell, B., & Peters, F. (2023) · Die Onkologie · doi:10.1007/s00761-023-01449-z
Assessment on Factors That Affect the Cardiovascular Patients Using Cox Ph ModelPreprint
Hirbo, H. G., Kuse, K. A., Hola, R. N., & Oromo, H. H. (2023) · Research Square · doi:10.21203/rs.3.rs-3809090/v1
Government Financing Mechanism for SMEs Through Convertible Loans: The Case of the UK Future FundPreprint
Káčer, M., Mohamed, A., Schwienbacher, A., & Wilson, N. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4683934
Survival Analysis of Breast Cancer Patients: A Population-Based Study from SEER
Rahman, M. S., Keats, A. I., Kabir, M. A., Newaz, A., & Islam, M. M. (2023) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet58911.2023.10389236
Recommendation Algorithm Based on Survival Action Rules
Hermansa, M., Sikora, M., Sikora, B., & Wróbel, Ł. (2024) · Applied Sciences · doi:10.3390/app14072939
“Multivariate statistical methods, on the other hand, take into account more than one factor and allow the strength of the impact of each factor to be determined [7].”
Methodde Freitas, C. S. M., de Souza, A. D., Rocha-Silva, F., da Silva, T. A. M., & Soares, A. N. (2024) · Journal of Advances in Medicine and Medical Research · doi:10.9734/jammr/2024/v36i55433
Zhu, Z., & Lu, J. (2024) · Medicine · doi:10.1097/md.0000000000038116
“[31] According to the final multivariate Cox model, we calculated risk scores by using the following method: (cid:31) where Exp i represented the editing level of sites, and Coe i represented the multivariate Cox regression coefficient of corresponding sites.”
MethodEfficiency of a New Parametric Cox Proportional Hazard Model Using Monte Carlo Simulation Study
Ibeakuzie, P. O., & Onyeagu, S. I. (2024) · Earthline Journal of Mathematical Sciences · doi:10.34198/ejms.14424.817839
On pathogenicity with varying microbial virulence and host resistancePreprint
Gupta, A., & Unckless, R. L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.02.616271
EXploring the journeys of Patients who End their Calls prior to Triage by NHS111: The EXPECT studyPreprint
Pilbery, R., Lewis, J., & Simpson, R. M. (2024) · medRxiv · doi:10.1101/2024.10.10.24315243
Venishetty, N., Rafati, Y., & Alzweri, L. M. (2024) · Uro · doi:10.3390/uro4040014
“Multivariate Cox proportional hazard models were performed with the dependent Multivariate Cox proportional hazard models were performed with the dependent variable being time overall or cancer-specific death [16]. being time overall or cancer-speci fi c death [16].”
Sung, J., Kim, K. W., Han, K., & Park, C. (2024) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2024.0681
“In our case, survival data derived from health insurance claims exhibit high variability, and it is unlikely that they conform consistently to any particular parametric distribution [4].”
The Demography, Longevity and Mortality of Bullmastiffs Attending Veterinary Practices in Australia
Carney, A., Williamson, P., & Taylor, R. M. (2024) · Animals · doi:10.3390/ani14233419
Alawad, T. A. M., Xue, F., Hu, X., Tian, Z., Zhao, Y., Li, X., Guan, L., Guan, Y., Liu, J., & Ahmad, I. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5038729
Flexibility versus Simplicity: A Comparative Study of Survival Models for HIV AIDS Failure Rates
Mwirigi, N. (2025) · Open Journal of Modelling and Simulation · doi:10.4236/ojmsi.2025.131004
Prognostic and biological roles of Parkinson’s disease genes in cancerPreprint
Fiume, S., Molinari, F., Vai, B., Poletti, S., Citterio, G., Fiumara, F., Papin, S., Paganetti, P., Callari, M., & Colnaghi, L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.22.644759
A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (Trashfire)
Meyers, C., Sedghpour, M. R. S., Löfstedt, T., & Elmroth, E. (2024) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc63072.2024.10935101
“Assume that the cost of training a model, C train , is a function of the total training time, T train , the number of training samples, N train , and the training time per sample, t train = T train N train , such that the cost of training on hardware with a fixed time-cost is where C h is the cost…”
Can interpretability and accuracy coexist in cancer survival analysis?Preprint
Borole, P., Wang, T., Vergari, A., & Rajan, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.11.648380
“Traditional methods like 1 the Cox Proportional Hazards (CPH) model, a linear approach, have been widely used due to their straightforward interpretability and robust ability to estimate hazard ratios (a type of relative risk) [2, 3].”
Cui, S., Liu, W. Y., Wu, C., & Wang, X. (2025) · Hepatology International · doi:10.1007/s12072-025-10844-0
“Current statistical best practices recommend verification of the PH assumption through Schoenfeld residual tests or residual plots, as extensively documented in the literature [2].”
Meng, S., Li, X., Zhang, J., & Cheng, X. (2025) · International Journal of General Medicine · doi:10.2147/ijgm.s508059
“12,13 Although univariate Cox analysis cannot account for gene interactions, it is an efficient initial screening tool to identify candidate genes significantly associated with survival (p 0.05), reducing dimensionality for subsequent modeling.”
InfluentialDavid E. Freeman MVB, M. (2025) · doi:10.1002/9781119104230.ch46
Kobori, A., Makita, C., Tanaka, O., Okada, S., Kajiura, Y., Yamada, N., & Matsuo, M. (2025) · Asia-Pacific Journal of Clinical Oncology · doi:10.1111/ajco.14206
Earl, J. C., Hart, L. A., & Hansen, R. N. (2025) · Journal of Managed Care & Specialty Pharmacy · doi:10.18553/jmcp.2025.31.8.741
Tohmeh, M., Haber, M., Nasr, M., Kanaan, E., Trétarre, B., Foucan, A.-S., & Borie, F. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5366121
Ali, M., khan, A. A., Syed, R., Noor, M., & Ahmad, B. (2025) · Wiener klinische Wochenschrift · doi:10.1007/s00508-025-02594-8
Mia, M., Ahmed, I., Sabiha, S. A., Maria, S. I., & Reza, A. W. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6300-2_37
Kummetha, V. C., & Concas, S. (2025) · Journal of Transportation Safety & Security · doi:10.1080/19439962.2025.2554090
“…safety research i.e., Kaplan-Meier survival estimate (univariate: non-parametric), Cox proportional hazards model (multivariate: semi-parametric), and accelerated failure time model (multivariate: parametric) (Bradburn et al., 2003; Clark et al., 2003; Parmet et al., 2014; Shangguan et al., 2020).”
Majeed, S., & Tu, J. (2025) · Internal and Emergency Medicine · doi:10.1007/s11739-025-04114-y
Thériault, M., Santerre, K., Brousseau, N., Rochette, S., Omar, R. F., Pelletier, J. N., Gilbert, C., Masson, J., Baz, M., Boudreau, D., et al. (2025) · Infectious Disease Reports · doi:10.3390/idr17050122
“The Cox proportional hazards model is designed to accommodate time-varying effects common to all participants, making the calendar time scale of vaccine uptake an appropriate metric for comparisons across participants [29–31].”
Lactulose, Rifaximin, and Survival in Hepatic Encephalopathy: A Cohort Study of 120 Patients
Alexandrescu, L., Tofolean, I. T., Aftenie, C., Alexandrescu, D. M., Tofolean, D. E., Nicoara, A. D., Stăniguț, A. M., Twakor, A. N., Rusu, E. E., Dumitru, E., et al. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14207331
“A p -value < 0.05 was considered statistically significant [14].”
InfluentialUse of Survival Analysis and Machine Learning in Stage III Non-Small Cell Lung Cancer.
Gucema, A. C., Sosa‐Fajardo, P., Ortega, J. A., Casin, I. O., & Guerra, J. L. L. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.10.064
Las publicaciones médicas por dentro
de Antioquia, U., & Jaimes, F. A. (2025) · Editorial Universidad de Antioquia eBooks · doi:10.17533/udea.978-958-501-247-9
Ekström, N. (2014) · Gothenburg University Publications Electronic Archive (Gothenburg University)
Briolat, E. S., Arenas, L. M., Hughes, A. E., Liggins, E., & Stevens, M. (2021) · Figshare · doi:10.6084/m9.figshare.15102625
Cancer Survival: Analysis and Reporting
Zabor, E. C. (2025) · Springer eBooks · doi:10.1007/978-3-031-75907-9_2
Briolat, E. S., Arenas, L. M., Hughes, A. E., Liggins, E., & Stevens, M. (2021) · Figshare · doi:10.6084/m9.figshare.15102613
Huang, Y., Li, J., Li, M., & Aparasu, R. R. (2024) · Figshare · doi:10.6084/m9.figshare.26639016
Private equity ownership and entrepreneurial firms’ crash riskPreprint
Amini, S., Mohamed, A., Schwienbacher, A., & Wilson, N. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4742810
Briolat, E. S., Arenas, L. M., Hughes, A. E., Liggins, E., & Stevens, M. (2021) · Figshare · doi:10.6084/m9.figshare.15102622
Zheng, H., Zhu, J., Xie, W., & Zhong, J. (2021) · Figshare · doi:10.6084/m9.figshare.17273344
Briolat, E. S., Arenas, L. M., Hughes, A. E., Liggins, E., & Stevens, M. (2021) · Figshare · doi:10.6084/m9.figshare.15102610
COŞAR, R., Süt, N., Parlar, Ş. E., Özgüven, Y., Nurlu, D., Tastekin, E., Batu, S., Şenödeyici, E., Özler, T., Dedeli, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26739579
Huang, Y., Li, J., Li, M., & Aparasu, R. R. (2024) · Figshare · doi:10.6084/m9.figshare.26639010
Huang, Y., Li, J., Li, M., & Aparasu, R. R. (2024) · Figshare · doi:10.6084/m9.figshare.26639007
Huang, Y., Li, J., Li, M., & Aparasu, R. R. (2024) · Figshare · doi:10.6084/m9.figshare.26639013
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.
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
