Journal articles: 'Survival curves'

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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 adjusted survival curves which take possible distortions in the data set into account. The benefits of adjusted survival curves in assessing potentially prognostic factors are elucidated by the application to data from renal transplantation.

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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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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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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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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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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 construct the expected survival curve for each treatment arm of the ICON2 trial. Expected survival curves were compared with observed survival curves in the ICON2 trial at all time points using a nonparametric test. Results: The prognostic model for survival obtained in the meta-analysis included extent of residual disease, age, histologic grade, and International Federation of Gynecology and Obstetrics stage. When this model was applied to the ICON2 data, there was no difference between the expected and observed curves in the CAP arm. In contrast, the observed survival curve for carboplatin was far superior to the expected survival curve for CP (P < .01). Conclusion: These analyses provide indirect evidence that better results are achieved with carboplatin alone at an optimally tolerated dose, compared with the CP combination at a cisplatin dose of 50 to 60 mg/m2. The expected survival may provide valuable insight when direct comparisons between randomized groups yield discrepant results across different studies.

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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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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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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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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 GraphPad Prism software for statistical data processing. It is argued that Kaplan – Meyer curves could provide an effective tool in simulating the patient survival in case of COVID-19 by various criteria of grouping: gender (male and female); treatment method; associated diseases (diabetes and others); age group; vaccinated or not vaccinated patients etc.  The significance of differences between survival curves of patienst in various groups can be found using Log-Rank test, Gehan – Wilcoxon test, Mantel – Cox test and others. The results of tests produced on the basis of data on 42 patients ill with leukemia show significant differences in the survival between two groups of patients. This confirms the assumption that the new method of treatment is more effective than the conventional one. The main deficiency of the nonparametric method of Kaplan – Meyer is that it is incapable to build curves by several categorical variables. The main advantages of Cox regression based on the Cox proportional hazards model are demonstrated.

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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 plots (difference in survival probability and risk difference, difference in restricted mean survival time, landmark analyses, and hazard ratio over time), along with KM curve. Results. Entanglement and intersection of two KM curves would make the 5 complementary plots to fluctuate over time intuitively. Absolute effects were presented in the 3 plots of difference in survival probability, risk, and restricted mean survival time. Changed P values from landmark analyses were used to inspect conditional treatment effect; the turning points could be identified for further landmark analysis. When proportional hazard assumption was not met, estimated hazard ratio from traditional Cox regression was not appropriate, and time-varying hazard ratios could be presented instead of an average and single value. Conclusions. The 5 complementary plots with KM curve give a broad and straightforward picture of potential time-varying effect. They will provide clear insight in treatment effect and assist clinicians to make decision comprehensively.

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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 (≥75%) or low ( < 30%) probability of 2 phase decay, depending on drug type (p < 0.0001, Table). 11/11 PD1/ipilimumab combinations had 2 phase decay vs 36/209 curves (17%) for all other combinations. Conclusions: Drugs have either high or low probability of PFS curve 2 phase decay. Clinical trial methods or some mechanisms of acquired resistance might contribute to 2 phase decay, but 2 phase decay also could indicate a dichotomous factor (eg gene mutation/deletion or complete pathway silencing) producing 2 distinct subpopulations with differing progression rates. Drugs with high 2 phase decay could be prime candidates for RNA & whole genome sequencing, pathway expression studies etc to identify dichotomous predictive factors. Further assessment is needed to better understand why some drugs behave differently when given in combinations vs as single agents. [Table: see text]

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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 harder to interpret. In immunotherapy trials, mPFS is a poor surrogate for mOS, and both may underrepresent the clinical benefit over time. Methods: We aimed to develop novel metrics that more effectively represent the Kaplan-Meier estimate. The graphical curve of the Kaplan-Meier estimate may be represented by the equation as a function of time. The integration of f ( x), which mathematically represents the area under the curve and clinically the cumulative or integrated survival benefit with time, is: ∫0t f( x) dx = F(t) − F(0). Kaplan-Meier curve equation with constants C and A may be approximated by: ∫0t C e-A x dx = C e-A t ]0 t = C( e-A t – 1). The average integrated clinical benefit with time is: 1/ t ∫0t f( x) dx = [ F( t) − F(0)]/ t. Results: The Phase 3 KeyNote-061 trial of metastatic gastric cancer treated with single agent Pembrolizumab immunotherapy versus Paclitaxel chemotherapy was use for integrated PFS and iOS calculation. The Kaplan-Meier curves intersected or crossed in KeyNote-061 trial. We show the established ORR, mPFS, mOS, DOR. Integrated PFS (iPFS) and iOS were calculated with greater increase in iOS and much less decrease in iPFS for Pembrolizumab than Paclitaxel treatment. Conclusions: Integrated survival is a novel analysis that integrate the Kaplan Meier estimates and better represent the entire median survival and drug DOR may be combined into a single useful metric for overall clinical benefit. [Table: see text]

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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 uncertainty was addressed.MethodsA systematic search was completed on the NHS Evidence Search webpage limited to single technology appraisals of cancer interventions published in 2017, with information obtained from the NICE Web site.ResultsTwenty-eight appraisals were identified, covering twenty-two interventions across eighteen diseases. Every economic model used parametric curves to model survival. All submissions used goodness-of-fit statistics and plausibility of extrapolations when selecting a parametric curve. Twenty-five submissions considered alternate parametric curves in scenario analyses. Six submissions reported including the parameters of the survival curves in the probabilistic sensitivity analysis. ERGs agreed with the company's choice of parametric curve in nine appraisals, and agreed with all major survival-related assumptions in two appraisals.ConclusionsTSD 14 on survival extrapolation was followed in all appraisals. Despite this, the choice of parametric curve remains subjective. Recent developments in Bayesian approaches to extrapolation are not implemented. More precise guidance on the selection of curves and modelling of uncertainty may reduce subjectivity, accelerating the appraisal process.

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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 entry. Chronological age survival curves differ significantly among temperatures. Survivorship at early age after hatch is greatest at lower temperatures and declines as temperature increases. Mean longevity was 220 (±13 SEM) days at 9.7°C. Physiological age survival curves constructed with developmental base temperature (7.2°C) did not correspond to those constructed with a senescence base temperature (5.9°C). A single degree day survival curve with an appropriate temperature threshold based on senescence adequately describes survivorship under non-stress temperature conditions (5.9–24.9°C).

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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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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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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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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 curves. Simulation studies are performed to compare the proposed method with existing methods. Furthermore, the advantage of the new test is finally exemplified in the analysis of a β-thalassaemia major data.

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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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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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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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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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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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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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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 the most up-to-date 10-year relative survival curves that might have been obtained in 1983 to 1987 using either traditional (cohort-wise) or period analysis. We also give the most recent 10-year survival curves obtained by period analysis for the 1993 to 1997 period. RESULTS: For all forms of cancer, period analysis of the 1983 to 1987 data yielded survival curves that were very close to the survival curves later observed for patients who were newly diagnosed in that period (median and maximum difference of 10-year relative survival estimates: 0.9 and 5.7 percent units, respectively). By contrast, the survival curves obtained by traditional (cohort-wise) survival analysis in 1983 to 1987 would have been much lower for most forms of cancer (median and maximum difference: 5.8 and 18.4 percent units, respectively). The 10-year survival curves for the 1993 to 1997 period are substantially more favorable than previously available, traditionally derived survival curves for most forms of cancer. CONCLUSION: Period analysis is a useful tool for deriving up-to-date long-term survival curves of patients with cancer.

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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 access to the IPD from a published randomized clinical trial (n=694) testing therapies (anastrozole and fulvestrant) for metastatic breast cancer with 10 years of follow-up on progression-free survival (PFS) and overall survival (OS) outcomes. After adjusting the original IPD, we sought to fit models to published aggregated data (Kaplan–Meier curves) using nonlinear regressions and optimization algorithms. Both methods were compared in terms of visual inspection and statistical fit quality (Akaike information criterion [AIC] and Bayesian information criterion [BIC]).ResultsSurvival curves directly adjusted to aggregated data showed a visually similar profile compared to IPD adjustments. According to AIC/BIC values, Weibull and generalized gamma distributions best fit OS data, both in individualized and aggregated approaches. For PFS, log-logistic and log-normal curves were the best choices for the anastrozole arm, and for fulvestrant, the best choices were log-normal and generalized gamma for individualized data, and Gompertz and generalized gamma for the aggregated method. The proposed R language code proved to be reproducible and amenable to automation in future HTA applications.ConclusionsDirectly adjusting survival curves to aggregated data is a simple and useful alternative in situations where access to IPD is not feasible.

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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 individual element. The value of one of these parameters, namely the coordinates of the inflection point of human survival curves, indicates that, apparently, both in long-lived species in general and in humans specifically, the ageing promoting mechanism is the progressive deterioration of reliability - the dilapidation of the elements (cells) crucial for ageing.

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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 calibration curve, decision curve analysis (DCA), and Kaplan–Meier (K-M) curve analysis were used to evaluate the nomograms. Results. Four variables were identified as independent prognostic factors for OS, and three variables were identified as independent prognostic factors for CSS. The receiver operating characteristic (ROC) curves demonstrated the strong discriminatory power of the nomograms. The calibration and DCA curves showed that the nomograms had a satisfactory ability to predict OS and CSS. The K-M curves showed that age, gender, primary site, chemotherapy, and tumor stage affected patient survival. Conclusions. In patients with PD-DLBCL, age, race, primary site, and chemotherapy affected OS, while age, race, and chemotherapy affected CSS. The two nomograms created based on the aforementioned variables provided more accurate individual survival predictions for PD-DLBCL patients and can help physicians make appropriate clinical decisions.

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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 that interface with biological sciences and medicine in order to guarantee proficiency in one of the most popular yet frequently misunderstood methods in clinical research. Being familiar with these concepts is not only essential for designing new clinical studies but also for critically assessing and interpreting published data.

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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-born gender and weight affects the classification according to the CRIB scale, the authors use the direct method. The authors sampled 160 Portuguese babies. Findings – The smoothing applied to the ROC curves indicates that the curve's original shape does not change when a bandwidth h=0.1 is used. Furthermore, gender seems to be a significant covariate in predicting baby deaths. A higher value was obtained for the area under curve (AUC) when conditional on female babies. Practical implications – The challenge is to determine whether gender discriminates between dead and surviving babies. Originality/value – The authors constructed empirical ROC curves for CRIB data and empirical ROC curves conditioned on gender. The authors calculate the corresponding AUC and tested the difference between them. The authors also constructed smooth ROC curves for two approaches.

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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 outcomes published after 2005 was conducted. All Kaplan-Meier (KM) survival curves were analyzed. Individual patient data was extracted from the survival curves and a cumulative survival curve was generated with corresponding times calculated. The primary outcome for all included RCT was mortality at longest follow-up. RESULTS We identified 14 RCT which contained KM curves presenting sufficient data for individual patient survival data extraction. Survival data of a total of 2294 patients was extracted with 1396 (60.85%) patients in the treatment arms and 898 in the control arms (39.15%). Patients in the control arm received the standard care of GBM management, whereas those in the treatment arms received differing experimental regimens. After generating the cumulative survival curve, the median survival time for patients in the treatment arm was found to be 15.2 months and 11.2 months for the control arm (P &lt; 0.0001). The two-year survival rate was 31.0% for the treatment arm and 15.6% for the control arm. CONCLUSION The survival times observed in GBM patients receiving the standard care of management were worse than anticipated. In general, however, overall estimates of survival among patients with GBM have improved with more patients surviving to 2 years.

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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 terms of survival was demonstrated by the area under the curve (AUC) of the receiver operating characteristic curves. In addition, the prediction consistency and clinical value of the nomogram were validated by calibration curves and decision curve analysis. Results. All included patients were divided into a training set (n = 308) and a validation set (n = 131). Based on univariate and multivariate analyses, we determined that age, race, grade, and historic stage were independent risk factors for overall survival after surgery in patients with fibrosarcoma. The AUC of the receiver operating characteristic curves demonstrated the high predictive accuracy of the prognostic nomogram, while the decision curve analysis revealed the high clinical application of the model. The calibration curves showed good agreement between predicted and observed survival rates. Conclusion. We developed a new nomogram to estimate 1-year, 3-year, and 5-year OS based on the independent risk factors. The model has good discriminatory performance and calibration ability for predicting the prognosis of patients with fibrosarcoma after surgery.

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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 photon radiation and 72 h for 186Re. The biphasic survival curve after photon radiation exposure can be explained by radiation hypersensitivity at doses below 1 Gy resulting in a D0 of 3.3 Gy. Doses exceeding 1.0 Gy showed a D0 of 10 Gy. The biphasic survival curve in case of 186Re incubation represents repair of sub lethal damage in the first section of the curve (D0 11.1 Gy) – in this case, biological effects of the β--emitter are attenuated by repair. Beyond an accumulated dose of 1.6 Gy, 186Re showed a steeper slope with a D0 of 4 Gy, corresponding to 2.5 times higher biological effects compared to acute photon irradiation (10 Gy). Conclusion Low dose rate radiation resulted in low biological effects at low doses. There is a threshold of accumulated dose above which biological effects of 186Re-incubation exceed those of photon irradiation.

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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 survival process for an individual. However, due to the lack of ground-truth regarding the underlying stochastic process of survival for an individual, optimizing and measuring calibration in survival analysis is an inherently difficult task. In this work, we i) highlight the shortcomings of existing approaches in terms of calibration and ii) propose a new training scheme for optimizing deep survival analysis models that maximizes discriminative performance, subject to good calibration. Compared to state-of-the-art approaches across two publicly available datasets, our proposed training scheme leads to significant improvements in calibration, while maintaining good discriminative performance.

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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–Social Security Administration team in the identification of medical conditions to prioritize for adjudication in disability benefits processing.

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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 class imbalance. We improve upon traditional survival machine learning approaches through a novel framework called classification-augmented survival estimation (CASE), which treats survival as a classification task that ultimately yields survival curves, beginning with dataset augmentation to improve class imbalance for use with any classification model. Unlike other approaches, CASE additionally provides an exact survival time prediction. We demonstrate CASE on a liver transplant case study to predict >20 years survival post-transplant, finding that CASE dataset augmentation improved AUCs from 0.69 to 0.88 and F1 scores from 0.32 to 0.73. Compared to Kaplan-Meier, Cox, and RSF survival models, the CASE framework demonstrated better performance across various existing survival metrics, as well as our novel metric, mean of individual areas under the survival curve (mAUSC). Further, we develop novel temporal feature importance methods to understand how different features may vary in survival importance over time, potentially providing actionable insights in real-world survival problems.

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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 variation. A modification of the AFT model that uses temporal scaling as the null hypothesis made it possible to distinguish between the quantitative and qualitative differences in the dynamics of aging. It was also shown that it is possible to compare the data on the survival of species characterized by the survival curves of the original shape (i.e., “flat” curves without a pronounced increase in the probability of death with age typical of slowly aging species), when considering the distribution of lifespan as a statistical random variable and comparing parameters of such distribution. Thus, it was demonstrated that the higher impact of mortality caused by external factors (background mortality) in addition to the age-dependent mortality, the higher the disorder of mortality values and the greater its difference from the calculated value characteristic of developed countries (15-20%). For comparison, CVLS for the Paraguayan Ache Indians is 100% (57% if we exclude prepuberty individuals as suggested by Jones et al.). According to Skulachev, the next step is considering mortality fluctuations as a measure for the disorder of survival data. Visual evaluation of survival curves can already provide important data for subsequent analysis. Thus, Sokolov and Severin [1] found that mutations have different effects on the shape of survival curves. Type I survival curves generally retains their standard convex rectangular shape, while type II curves demonstrate a sharp increase in the mortality which makes them similar to a concave exponential curve with a stably high mortality rate. It is noteworthy that despite these differences, mutations in groups I and II are of a similar nature. They are associated (i) with “DNA metabolism” (DNA repair, transcription, and replication); (ii) protection against oxidative stress, associated with the activity of the transcription factor Nrf2, and (iii) regulation of proliferation, and (or these categories may overlap). However, these different mutations appear to produce the same result at the organismal level, namely, accelerated aging according to the Gompertz’s law. This might be explained by the fact that all these mutations, each in its own unique way, either reduce the lifespan of cells or accelerate their transition to the senescent state, which supports the concept of Skulachev on the existence of multiple pathways of aging (chronic phenoptosis).

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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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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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Journal articles on the topic 'Survival curves'

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