Journal articles: 'Survival curves'

1

Hess, Aaron S., and John R. Hess. "Kaplan–Meier survival curves." Transfusion 60, no. 4 (February 20, 2020): 670–72. http://dx.doi.org/10.1111/trf.15725.

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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 (July 1998): 353–80. http://dx.doi.org/10.1080/10408699891274246.

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

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Chappell, Rick, and Xiaotian Zhu. "Describing Differences in Survival Curves." JAMA Oncology 2, no. 7 (July 1, 2016): 906. http://dx.doi.org/10.1001/jamaoncol.2016.0001.

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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 (June 29, 2012): 792–804. http://dx.doi.org/10.1177/0272989x12451057.

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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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Zelterman, Daniel, and James W. Curtsinger. "Survival Curves Subjected to Occasional Insults." Biometrics 51, no. 3 (September 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 (August 20, 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 (March 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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11

Buyse, Marc, Tomasz Burzykowski, Mahesh Parmar, Valter Torri, George Omura, Nicoletta Colombo, Chris Williams, Pierfranco Conte, and Jan Vermorken. "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 (May 1, 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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Stewart, David J., Dominick Bossé, Andrew George Robinson, Michael Ong, Stephanie Yasmin Brule, Michael Fung-Kee-Fung, John Frederick Hilton, Mark J. Clemons, and Alberto Ocana. "Population kinetics of progression free survival (PFS)." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 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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13

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

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14

Go, C. G., J. E. Brustrom, M. F. Lynch, and C. M. Aldwin. "Ethnic Trends in Survival Curves and Mortality." Gerontologist 35, no. 3 (June 1, 1995): 318–26. http://dx.doi.org/10.1093/geront/35.3.318.

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15

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

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16

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 (July 2010): 258–71. http://dx.doi.org/10.1002/jrsm.25.

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17

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

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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 (December 2004): 2070–75. http://dx.doi.org/10.1007/s10350-004-0743-4.

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19

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 (April 1994): 233–50. http://dx.doi.org/10.1016/0025-5564(94)90054-x.

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20

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 (May 2012): 1416–25. http://dx.doi.org/10.1016/j.athoracsur.2011.12.094.

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21

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

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22

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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23

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 (November 12, 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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24

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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25

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 (January 6, 2018): 6. http://dx.doi.org/10.3390/biology7010006.

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26

Peleg, Micha. "Microbial Survival Curves: Interpretation, Mathematical Modeling, and Utilization." Comments� on Theoretical Biology 8, no. 4-5 (July 1, 2003): 357–87. http://dx.doi.org/10.1080/08948550302436.

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27

Bissonnette, L., and J. de Bresser. "Eliciting Subjective Survival Curves: Lessons from Partial Identification." Journal of Business & Economic Statistics 36, no. 3 (May 16, 2017): 505–15. http://dx.doi.org/10.1080/07350015.2016.1213635.

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28

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

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29

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

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30

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

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31

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 (December 15, 2006): 818–27. http://dx.doi.org/10.1111/j.1749-6632.1961.tb50078.x.

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32

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

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33

Eng, Kevin H., and Brandon-Luke L. Seagle. "Covariate-Adjusted Restricted Mean Survival Times and Curves." Journal of Clinical Oncology 35, no. 4 (February 1, 2017): 465–66. http://dx.doi.org/10.1200/jco.2016.67.2279.

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34

Pruitt, Ronald C. "Identifiability of Bivariate Survival Curves from Censored Data." Journal of the American Statistical Association 88, no. 422 (June 1993): 573–79. http://dx.doi.org/10.1080/01621459.1993.10476309.

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35

Hsieh, Jin-Jian, and Hsin-Yu Chen. "A testing strategy for two crossing survival curves." Communications in Statistics - Simulation and Computation 46, no. 8 (January 20, 2017): 6685–96. http://dx.doi.org/10.1080/03610918.2017.1280167.

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36

Bos, C. J., P. Stam, and J. H. van der Veen. "Interpretation of UV-survival curves of Aspergillus conidiospores." Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis 197, no. 1 (January 1988): 67–75. http://dx.doi.org/10.1016/0027-5107(88)90141-8.

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37

Marino, Pietro. "In survival curves is the P-value enough?" Lung Cancer 12, no. 1-2 (March 1995): 87–89. http://dx.doi.org/10.1016/0169-5002(94)00411-f.

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38

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 (October 1994): 241–49. http://dx.doi.org/10.1007/bf00996604.

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39

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 (February 1, 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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40

PELEG, MICHA, and MARK D. NORMAND. "Calculating Microbial Survival Parameters and Predicting Survival Curves from Non-Isothermal Inactivation Data." Critical Reviews in Food Science and Nutrition 44, no. 6 (November 2004): 409–18. http://dx.doi.org/10.1080/10408690490489297.

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41

Zwick, Rebecca, and Jeffrey C. Sklar. "A Note on Standard Errors for Survival Curves in Discrete-Time Survival Analysis." Journal of Educational and Behavioral Statistics 30, no. 1 (March 2005): 75–92. http://dx.doi.org/10.3102/10769986030001075.

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Cox (1972) proposed a discrete-time survival model that is somewhat analogous to the proportional hazards model for continuous time. Efron (1988) showed that this model can be estimated using ordinary logistic regression software, and Singer and Willett (1993) provided a detailed illustration of a particularly flexible form of the model that includes one parameter per time period. This work has been expanded to show how logistic regression output can also be used to estimate the standard errors of the survival functions. This is particularly simple under the model described by Singer and Willett, when there are no predictors other than time.

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42

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 (October 7, 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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43

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 (November 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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Parker, Rodger, Charles Waldren, Tom K. Hei, D. F. Wong, and T. L. Puck. "Analysis of mutant frequency curves and survival curves applied to the AL hybrid cell system." Journal of Theoretical Biology 132, no. 1 (May 1988): 113–17. http://dx.doi.org/10.1016/s0022-5193(88)80194-2.

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45

Connock, Martin, Chris Hyde, and David Moore. "Cautions Regarding the Fitting and Interpretation of Survival Curves." PharmacoEconomics 29, no. 10 (October 2011): 827–37. http://dx.doi.org/10.2165/11585940-000000000-00000.

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46

Guyot, P., N. J. Welton, M. Beasley, and A. E. Ades. "Extrapolation of Trial-Based Survival Curves Using External Information." Value in Health 17, no. 7 (November 2014): A326. http://dx.doi.org/10.1016/j.jval.2014.08.587.

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Comfort, Alex. "SURVIVAL CURVES OF SOME BIRDS IN THE LONDON ZOO." Ibis 104, no. 1 (June 28, 2008): 115–17. http://dx.doi.org/10.1111/j.1474-919x.1962.tb08636.x.

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48

Sedgwick, P., and K. Joekes. "Kaplan-Meier survival curves: interpretation and communication of risk." BMJ 347, no. 29 1 (November 29, 2013): f7118. http://dx.doi.org/10.1136/bmj.f7118.

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49

Letón, E., and P. Zuluaga. "EQUIVALENCE BETWEEN SCORE AND WEIGHTED TESTS FOR SURVIVAL CURVES." Communications in Statistics - Theory and Methods 30, no. 4 (March 31, 2001): 591–608. http://dx.doi.org/10.1081/sta-100002138.

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50

Klein, John P., Brent Logan, Mette Harhoff, and Per Kragh Andersen. "Analyzing survival curves at a fixed point in time." Statistics in Medicine 26, no. 24 (2007): 4505–19. http://dx.doi.org/10.1002/sim.2864.

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Journal articles on the topic 'Survival curves'

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