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Journal articles on the topic 'Survival curves'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Hess, Aaron S., and John R. Hess. "Kaplan–Meier survival curves." Transfusion 60, no. 4 (2020): 670–72. http://dx.doi.org/10.1111/trf.15725.

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Bender, R., A. Schultz, R. Pichlmayr, and U. Grouven. "Application of Adjusted Survival Curves to Renal Transplant Data." Methods of Information in Medicine 31, no. 03 (1992): 210–14. http://dx.doi.org/10.1055/s-0038-1634871.

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Abstract:An important means in the analysis of survival time data is the estimation and graphical representation of survival probabilities. In this paper unifactorial parametric and non-parametric survival curve estimators and two types of adjusted survival curves based on a parametric multifactorial approach are applied to renal transplant data. It is shown that the resulting survival curves can differ substantially. The unifactorial survival curves yield biased results in case of serious disequilibrium in the data. This drawback of the unifactorial methods has been overcome by the use of adj

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3

Peleg, Micha, and Martin B. Cole. "Reinterpretation of Microbial Survival Curves." Critical Reviews in Food Science and Nutrition 38, no. 5 (1998): 353–80. http://dx.doi.org/10.1080/10408699891274246.

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4

Rakow, Tim, Rebecca J. Wright, Catherine Bull, and David J. Spiegelhalter. "Simple and Multistate Survival Curves." Medical Decision Making 32, no. 6 (2012): 792–804. http://dx.doi.org/10.1177/0272989x12451057.

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5

Chappell, Rick, and Xiaotian Zhu. "Describing Differences in Survival Curves." JAMA Oncology 2, no. 7 (2016): 906. http://dx.doi.org/10.1001/jamaoncol.2016.0001.

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6

Smith, David W. E. "The tails of survival curves." BioEssays 16, no. 12 (1994): 907–11. http://dx.doi.org/10.1002/bies.950161209.

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7

Buyse, Marc, Tomasz Burzykowski, Mahesh Parmar, et al. "Using the Expected Survival to Explain Differences Between the Results of Randomized Trials: A Case in Advanced Ovarian Cancer." Journal of Clinical Oncology 21, no. 9 (2003): 1682–87. http://dx.doi.org/10.1200/jco.2003.04.088.

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Purpose: A meta-analysis of randomized trials in advanced ovarian cancer showed a longer survival with cyclophosphamide, doxorubicin, and cisplatin (CAP) than with cyclophosphamide and cisplatin (CP; P = .009). In contrast, the results of the large International Collaborative Ovarian Neoplasm Study (ICON2) showed no survival difference between CAP and carboplatin (P = .98). In this article, we show how these discrepant results can be reconciled through the estimation of expected survival curves. Materials and Methods: A proportional hazards model, fitted to the meta-analysis data, was used to

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Huang, Qiao, and Chong Tian. "Visualizing Time-Varying Effect in Survival Analysis: 5 Complementary Plots to Kaplan-Meier Curve." Oxidative Medicine and Cellular Longevity 2022 (March 29, 2022): 1–12. http://dx.doi.org/10.1155/2022/3934901.

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Background. Kaplan-Meier (KM) curve has been widely used in the field of oxidative medicine and cellular longevity. However, time-varying effect might be presented in KM curve and cannot be intuitively observed. Complementary plots might promote clear insights in time-varying effect from KM curve. Methods. Three KM curves were identified from published randomized control trials: (a) curves diverged immediately; (b) intersected curves with statistical significance; and (c) intersected curves without statistical significance. We reconstructed individual patient data, and plotted 5 complementary

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9

Zelterman, Daniel, and James W. Curtsinger. "Survival Curves Subjected to Occasional Insults." Biometrics 51, no. 3 (1995): 1140. http://dx.doi.org/10.2307/2533013.

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COMFORT, A. "SURVIVAL CURVES OF MAMMALS IN CAPTIVITY." Proceedings of the Zoological Society of London 128, no. 3 (2009): 349–64. http://dx.doi.org/10.1111/j.1096-3642.1957.tb00329.x.

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11

Davies, Charlotte, Andrew Briggs, Paula Lorgelly, Göran Garellick, and Henrik Malchau. "The “Hazards” of Extrapolating Survival Curves." Medical Decision Making 33, no. 3 (2013): 369–80. http://dx.doi.org/10.1177/0272989x12475091.

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12

Albers, W. "Comparing Survival Curves Using Rank Tests." Biometrical Journal 33, no. 2 (1991): 163–72. http://dx.doi.org/10.1002/bimj.4710330205.

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13

HOLUBOVA, H. "Kaplan-Meyer Survival Curves: Simulation Technique." Scientific Bulletin of the National Academy of Statistics, Accounting and Audit, no. 3-4 (December 21, 2021): 15–22. http://dx.doi.org/10.31767/nasoa.3-4-2021.02.

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The right censoring of survival data, being the most conventional method of research, is analyzed. The patient survival is explored in a time span that is shorter in fact than the actual survival time. However, when the actual survival time is unknown, the proxy of the observable survival time will be used for estimating the actual survival time.  The algorithm for estimation of survival probabilities is demonstrated by data on 20 patients during six months, with visualizing the technique of simulating Kaplan – Meyer curves by categorical variables (method of treatment and gender) using

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14

Stewart, David J., Dominick Bossé, Andrew George Robinson, et al. "Population kinetics of progression free survival (PFS)." Journal of Clinical Oncology 37, no. 15_suppl (2019): e18251-e18251. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e18251.

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e18251 Background: We assessed drug type impact on whether PFS curves could be fit by 2 phase decay models on nonlinear regression analysis (NLRA). Methods: We digitized 894 published PFS curves for incurable cancers. We used GraphPad Prism 7 for 1 phase and 2 phase decay NLRA, with constraints Y0 = 100 and plateau = 0. We defined curves as fitting 2 phase models if each subpopulation was ≥1% of the entire population and if subpopulation half-lives differed by a factor of ≥2, or if log-linear plots demonstrated unequivocal 2 phase decay. Results: PFS curves for single agents showed either high

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15

Gallacher, Daniel, Peter Auguste, and Martin Connock. "How Do Pharmaceutical Companies Model Survival of Cancer Patients? A Review of NICE Single Technology Appraisals in 2017." International Journal of Technology Assessment in Health Care 35, no. 2 (2019): 160–67. http://dx.doi.org/10.1017/s0266462319000175.

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AbstractObjectivesBefore an intervention is publicly funded within the United Kingdom, the cost-effectiveness is assessed by the National Institute of Health and Care Excellence (NICE). The efficacy of an intervention across the patients’ lifetime is often influential of the cost-effectiveness analyses, but is associated with large uncertainties. We reviewed committee documents containing company submissions and evidence review group (ERG) reports to establish the methods used when extrapolating survival data, whether these adhered to NICE Technical Support Document (TSD) 14, and how uncertain

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Chiu, Vi Kien, and Adele Lerolle-Chiu. "Integrated survival analysis." Journal of Clinical Oncology 41, no. 16_suppl (2023): e14695-e14695. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e14695.

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e14695 Background: Clinical trial efficacy may be determined by the primary endpoint(s) of median progression free survival (mPFS) and/or median overall survival (mOS) from the Kaplan-Meier estimate, and secondary endpoints of overall response rate (ORR) and duration of response (DOR). In the treatment of metastatic cancers, single agent immunotherapy tends to have lower ORR and longer DOR in comparison to chemotherapy or targeted tyrosine kinase inhibitor therapy, which tends to have higher ORR and shorter DOR. This leads to intersecting or crossing Kaplan-Meier survival curves, which are har

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17

Gómez, N. N., R. C. Venette, J. R. Gould, and D. F. Winograd. "A unified degree day model describes survivorship of Copitarsia corruda Pogue & Simmons (Lepidoptera: Noctuidae) at different constant temperatures." Bulletin of Entomological Research 99, no. 1 (2008): 65–72. http://dx.doi.org/10.1017/s0007485308006111.

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AbstractPredictions of survivorship are critical to quantify the probability of establishment by an alien invasive species, but survival curves rarely distinguish between the effects of temperature on development versus senescence. We report chronological and physiological age-based survival curves for a potentially invasive noctuid, recently described as Copitarsia corruda Pogue & Simmons, collected from Peru and reared on asparagus at six constant temperatures between 9.7 and 34.5°C. Copitarsia spp. are not known to occur in the United States but are routinely intercepted at ports of ent

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18

Kumazawa, S. "A new model of shouldered survival curves." Environmental Health Perspectives 102, suppl 1 (1994): 131–33. http://dx.doi.org/10.1289/ehp.94102s1131.

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19

Go, C. G., J. E. Brustrom, M. F. Lynch, and C. M. Aldwin. "Ethnic Trends in Survival Curves and Mortality." Gerontologist 35, no. 3 (1995): 318–26. http://dx.doi.org/10.1093/geront/35.3.318.

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20

Heagerty, Patrick J., and Yingye Zheng. "Survival Model Predictive Accuracy and ROC Curves." Biometrics 61, no. 1 (2005): 92–105. http://dx.doi.org/10.1111/j.0006-341x.2005.030814.x.

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21

Zaman, Qamruz, Nisar Ullah, Syed Habib Shah, Muhammad Ali, Muhammad Irshad, and Summayyia Azam. "Nonparametric test for multiple crossing Survival Curves." VFAST Transactions on Mathematics 12, no. 1 (2024): 349–65. http://dx.doi.org/10.21015/vtm.v12i1.1839.

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Log-rank, Wilcoxon and Tarone-Ware tests are most commonly used tests for testing the overall homogeneity of survival curves, but in certain situation it appears that they have a significant loss of statistical testing power. One such case is the more than one time crossing of survival curves. The problem considered often occurs in medical research. To overcome this problem, in this article, we present and study a non-parametric test procedure based on a new weight. The proposed new weighted test has greater power to detect overall differences between more than one time crossing survival curve

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22

MacKenzie, Todd A., Jeremiah R. Brown, Donald S. Likosky, YingXing Wu, and Gary L. Grunkemeier. "Review of Case-Mix Corrected Survival Curves." Annals of Thoracic Surgery 93, no. 5 (2012): 1416–25. http://dx.doi.org/10.1016/j.athoracsur.2011.12.094.

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23

Cole, Stephen R., and Miguel A. Hernán. "Adjusted survival curves with inverse probability weights." Computer Methods and Programs in Biomedicine 75, no. 1 (2004): 45–49. http://dx.doi.org/10.1016/j.cmpb.2003.10.004.

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24

Bellavia, Andrea, Matteo Bottai, Andrea Discacciati, and Nicola Orsini. "Adjusted Survival Curves with Multivariable Laplace Regression." Epidemiology 26, no. 2 (2015): e17-e18. http://dx.doi.org/10.1097/ede.0000000000000248.

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25

Zelterman, Daniel, Patricia M. Grambsch, Chap T. Le, Jennie Z. Ma, and James W. Curtsinger. "Piecewise exponential survival curves with smooth transitions." Mathematical Biosciences 120, no. 2 (1994): 233–50. http://dx.doi.org/10.1016/0025-5564(94)90054-x.

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26

Platell, Cameron F. E., and James B. Semmens. "Review of Survival Curves for Colorectal Cancer." Diseases of the Colon & Rectum 47, no. 12 (2004): 2070–75. http://dx.doi.org/10.1007/s10350-004-0743-4.

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27

Ouwens, Mario J. N. M., Zoe Philips, and Jeroen P. Jansen. "Network meta-analysis of parametric survival curves." Research Synthesis Methods 1, no. 3-4 (2010): 258–71. http://dx.doi.org/10.1002/jrsm.25.

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28

Brenner, Hermann, and Timo Hakulinen. "Up-to-Date Long-Term Survival Curves of Patients With Cancer by Period Analysis." Journal of Clinical Oncology 20, no. 3 (2002): 826–32. http://dx.doi.org/10.1200/jco.2002.20.3.826.

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PURPOSE: Provision of up-to-date long-term survival curves is an important task of cancer registries. Traditionally, survival curves have been derived for cohorts of patients diagnosed many years ago. Using data of the Finnish Cancer Registry, we provide an empirical assessment of the use of a new method of survival anlysis, denoted period analysis, for deriving more up-to-date survival curves. PATIENTS AND METHODS: We calculated 10-year relative survival curves actually observed for patients diagnosed with one of the 15 most common forms of cancer in 1983 to 1987, and we compared them with th

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Zimmermann, Ivan Ricardo, and Solange Borges. "PP78 Data Extrapolation With Survival Curves: An Alternative Approach With Aggregated Data." International Journal of Technology Assessment in Health Care 40, S1 (2024): S87. https://doi.org/10.1017/s0266462324002472.

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IntroductionRecently, there has been considerable emphasis on survival curves for data extrapolation, especially in the field of economic evaluation in oncology. Common methods for adjusting survival curves are complex and heavily reliant on individual patient data (IPD), which may not be feasible for health technology assessment (HTA). We propose an alternative method for survival curve extrapolation with direct adjustment to aggregated data.MethodsCommon parametric survival analysis models were tested: exponential, Weibull, log-normal, log-logistic, generalized gamma, and Gompertz. We had ac

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Chistyakov, Vladimir A., Yury V. Denisenko, Evgenia V. Prazdnova, and Sergey A. Emelyantsev. "Aging and Reliability: How Do Variations in the Failure Rate Affect the Shape of the Survival Curves of Aging Organisms." UNIVERSITY NEWS. NORTH-CAUCASIAN REGION. NATURAL SCIENCES SERIES, no. 2 (222) (June 27, 2024): 144–55. http://dx.doi.org/10.18522/1026-2237-2024-2-144-155.

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The article considers the regularities of the theory of reliability, which determine the constancy of the shape of the curves of organisms’ survival. The assumption that the probability of natural death is determined by the probability of accidental failure of a part of a series of homogeneous elements (cells) allows to obtain, analytically and through numerical experiments, survival curves similar to those observed in nature. The use of com-puter modelling methods makes it possible to identify the survival curve parameters determined by the dynamics of the probability of failure of an individ

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31

Yang, Xing-yao, Xin He, and Yun Zhao. "Nomogram-Based Prediction of Overall and Cancer-Specific Survival in Patients with Primary Bone Diffuse Large B-Cell Lymphoma: A Population-Based Study." Evidence-Based Complementary and Alternative Medicine 2022 (May 5, 2022): 1–9. http://dx.doi.org/10.1155/2022/1566441.

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Background. Primary bone diffuse large B-cell lymphoma (PD-DLBCL) accounts for more than 80% of primary bone lymphoma. We created two nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in patients with PD-DLBCL for this rare disease. Methods. In total, 891 patients diagnosed with PB-DLBCL between 2007 and 2016 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox analyses were performed to explore independent prognostic factors and create nomograms for OS and CSS. The area under the curve (AUC), the calib

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Gomes, António Pedro, Bruna Costa, Rita Marques, Vitor Nunes, and Constança Coelho. "Kaplan-Meier Survival Analysis: Practical Insights for Clinicians." Acta Médica Portuguesa 37, no. 4 (2024): 280–85. http://dx.doi.org/10.20344/amp.21080.

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This article aims to provide a guide that will help healthcare professionals and clinical researchers from all fields that deal with Kaplan-Meier curves. Survival analysis methods are among the most frequently used in the medical sciences and in clinical research. Overall survival, progression free survival, time to recurrence, or any other clinically relevant parameter represented by a Kaplan-Meier curve will be discussed. We will present a practical and straightforward interpretation of these curves, setting aside intricate mathematical considerations. Our focus will be on essential concepts

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Filipa Mourão, Maria, Ana Cristina Braga, and Pedro Nuno Oliveira. "CRIB conditional on gender: nonparametric ROC curve." International Journal of Health Care Quality Assurance 27, no. 8 (2014): 656–63. http://dx.doi.org/10.1108/ijhcqa-04-2013-0047.

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Purpose – The purpose of this paper is to use the kernel method to produce a smoothed receiver operating characteristic (ROC) curve and show how baby gender can influence Clinical Risk Index for Babies (CRIB) scale according to survival risks. Design/methodology/approach – To obtain the ROC curve, conditioned by covariates, two methods may be followed: first, indirect adjustment, in which the covariate is first modeled within groups and then by generating a modified distribution curve; second, direct smoothing in which covariate effects is modeled within the ROC curve itself. To verify if new-

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Chen, Yuyuan, Changxing Chi, Dedian Chen, et al. "Score for the Overall Survival Probability Scores of Fibrosarcoma Patients after Surgery: A Novel Nomogram-Based Risk Assessment System." Journal of Oncology 2021 (December 22, 2021): 1–9. http://dx.doi.org/10.1155/2021/4533175.

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Background. The primary purpose of this study was to determine the risk factors affecting overall survival (OS) in patients with fibrosarcoma after surgery and to develop a prognostic nomogram in these patients. Methods. Data were collected from the Surveillance, Epidemiology, and End Results database on 439 postoperative patients with fibrosarcoma who underwent surgical resection from 2004 to 2015. Independent risk factors were identified by performing Cox regression analysis on the training set, and based on this, a prognostic nomogram was created. The accuracy of the prognostic model in ter

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Hameed, Farrukh, Omar Sajjad, Sam Sathyamurthi, et al. "BIOS-07. SURVIVAL TIME OF GLIOBLASTOMA PATIENTS ENROLLED IN RANDOMIZED CONTROLLED TRIALS: A NEXT GENERATION META-ANALYSIS OF SURVIVAL CURVES." Neuro-Oncology 25, Supplement_5 (2023): v21—v22. http://dx.doi.org/10.1093/neuonc/noad179.0084.

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Abstract INTRODUCTION Glioblastoma is the most common primary malignant brain tumor and remains an incurable disease with high mortality. Randomized controlled trials (RCTs) have investigated novel therapies that have shown promise, but there have been no major additions in the treatment armamentarium in almost two decades. OBJECTIVE In a first study of its kind, we investigate the cumulative change in survival times over years for GBM patients by combining the survival outcome data published by multiple trials. METHODS A systematic database search of PubMed for GBM RCTs with significant outco

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Staudt, Jennifer, Christian Happel, Wolfgang Tilman Kranert, Benjamin Bockisch та Frank Grünwald. "Vergleich der biologischen Strahlenwirkung des β--Emitters 186Re mit 662 keV Photonenstrahlung auf die humane B-Zelllinie BV-173". Nuklearmedizin - NuclearMedicine 60, № 06 (2021): 438–44. http://dx.doi.org/10.1055/a-1560-2079.

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Abstract Aim Aim of the study was to determine the effects of the β--emitter 186Re and 662 keV photon radiation in order to compare the biological effects of low dose rate (186Re) to high dose rate irradiation. Methods Prae-B-lymphocytes were exposed to 662 keV photon radiation or incubated with a liquid solution of 186Re. Cell count and viability were compared over the observation period of seven days, survival curves constructed and analysed at time of lowest cell-viability. Results Biphasic cell survival curves resulted for both radiation types. Survival curves were obtained at 24 h for pho

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37

Kamran, Fahad, and Jenna Wiens. "Estimating Calibrated Individualized Survival Curves with Deep Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (2021): 240–48. http://dx.doi.org/10.1609/aaai.v35i1.16098.

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In survival analysis, deep learning approaches have been proposed for estimating an individual's probability of survival over some time horizon. Such approaches can capture complex non-linear relationships, without relying on restrictive assumptions regarding the relationship between an individual's characteristics and their underlying survival process. To date, however, these methods have focused primarily on optimizing discriminative performance and have ignored model calibration. Well-calibrated survival curves present realistic and meaningful probabilistic estimates of the true underlying

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38

Heuser, Aaron, Minh Huynh, and Joshua C. Chang. "Asymptotic convergence in distribution of the area bounded by prevalence-weighted Kaplan–Meier curves using empirical process modelling." Royal Society Open Science 5, no. 11 (2018): 180496. http://dx.doi.org/10.1098/rsos.180496.

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The Kaplan–Meier product-limit estimator is a simple and powerful tool in time to event analysis. An extension exists for populations stratified into cohorts where a population survival curve is generated by weighted averaging of cohort-level survival curves. For making population-level comparisons using this statistic, we analyse the statistics of the area between two such weighted survival curves. We derive the large sample behaviour of this statistic based on an empirical process of product-limit estimators. This estimator was used by an interdisciplinary National Institutes of Health–Socia

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39

Shourabizadeh, Hamed, Dionne M. Aleman, Louis-Martin Rousseau, Katina Zheng, and Mamatha Bhat. "Classification-augmented survival estimation (CASE): A novel method for individualized long-term survival prediction with application to liver transplantation." PLOS ONE 20, no. 1 (2025): e0315928. https://doi.org/10.1371/journal.pone.0315928.

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Survival analysis is critical in many fields, particularly in healthcare where it can guide medical decisions. Conventional survival analysis methods like Kaplan-Meier and Cox proportional hazards models to generate survival curves indicating probability of survival v. time have limitations, especially for long-term prediction, due to assumptions that all instances follow a general population-level survival curve. Machine learning classification models, even those designed for survival predictions like random survival forest (RSF), also struggle to provide accurate long-term predictions due to

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Shilovsky, G. A. "CALCULATION OF AGING: ANALYSIS OF SURVIVAL CURVES IN NORMAL AND IN PATHOLOGY, FLUCTUATIONS IN MORTALITY DYNAMICS, CHARACTERISTICS OF LIFE SPAN DISTRIBUTION AND INDICATORS OF ITS VARIATION." Биохимия 89, no. 2 (2024): 373–80. http://dx.doi.org/10.31857/s0320972524020138maruu.

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The article describes the history of studies of survival data carried out at the Research Institute of Physico-Chemical Biology under the leadership of Academician V. P. Skulachev from 1970s until present, with special emphasis on the last decade. The use of accelerated failure time (AFT) model and analysis of coefficient of variation of lifespan (CVLS) in addition to the Gompertz methods of analysis, allows to assess survival curves for the presence of temporal scaling (i.e., manifestation of accelerated aging), without changing the shape of survival curve with the same coefficient of variati

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Nowak, Stefan, Johannes Neidhart, Ivan Szendro, Jonas Rzezonka, Rahul Marathe, and Joachim Krug. "Interaction Analysis of Longevity Interventions Using Survival Curves." Biology 7, no. 1 (2018): 6. http://dx.doi.org/10.3390/biology7010006.

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Hamajima, N. "Comparing survival curves with subjects deviated from protocol." Japanese Journal of Biometrics 18, no. 1/2 (1997): 45–55. http://dx.doi.org/10.5691/jjb.18.45.

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Bissonnette, L., and J. de Bresser. "Eliciting Subjective Survival Curves: Lessons from Partial Identification." Journal of Business & Economic Statistics 36, no. 3 (2017): 505–15. http://dx.doi.org/10.1080/07350015.2016.1213635.

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Verbrugge, Lois M. "Survival Curves, Prevalence Rates, and Dark Matters Therein." Journal of Aging and Health 3, no. 2 (1991): 217–36. http://dx.doi.org/10.1177/089826439100300206.

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Chernick, Michael Ross, Erik Poulsen, and Yong Wang. "Effects of Bias Adjustment on Actuarial Survival Curves." Drug Information Journal 36, no. 3 (2002): 595–609. http://dx.doi.org/10.1177/009286150203600314.

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Peleg, Micha. "Microbial Survival Curves: Interpretation, Mathematical Modeling, and Utilization." Comments� on Theoretical Biology 8, no. 4-5 (2003): 357–87. http://dx.doi.org/10.1080/08948550302436.

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Hewitt, Harold B., and Charles W. Wilson. "SURVIVAL CURVES FOR TUMOR CELLS IRRADIATED IN VIVO*." Annals of the New York Academy of Sciences 95, no. 2 (2006): 818–27. http://dx.doi.org/10.1111/j.1749-6632.1961.tb50078.x.

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Peleg, M. "A Model of Survival Curves Having an'Activation Shoulder'." Journal of Food Science 67, no. 7 (2002): 2438–43. http://dx.doi.org/10.1111/j.1365-2621.2002.tb08757.x.

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Sakamoto, Maki, Kouhei Akazawa, Tatsuro Kamakura, Naoko Kinukawa, Yuko Nishioka, and Yoshiaki Nose. "Microsoft Excel Program for creating attractive survival curves." Journal of Medical Systems 18, no. 5 (1994): 241–49. http://dx.doi.org/10.1007/bf00996604.

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50

Gregory, WM. "Adjusting survival curves for imbalances in prognostic factors." British Journal of Cancer 58, no. 2 (1988): 202–4. http://dx.doi.org/10.1038/bjc.1988.193.

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