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Journal articles on the topic 'Survival test'

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Published: 14 March 2023

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1

GANS, CARL. "Survival test." Nature 349, no. 6305 (January 1991): 112. http://dx.doi.org/10.1038/349112b0.

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2

Fox, Jeffrey L. "Thrombolytic survival test." Nature Biotechnology 11, no. 2 (February 1993): 153. http://dx.doi.org/10.1038/nbt0293-153.

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3

Ziegler, A., S. Lange, and R. Bender. "Survival analysis: Log rank test." DMW - Deutsche Medizinische Wochenschrift 129 (November 2004): T4—T6. http://dx.doi.org/10.1055/s-2004-836075.

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4

Opdahl, Helge. "Survival put to the acid test." Critical Care Medicine 25, no. 8 (August 1997): 1431–36. http://dx.doi.org/10.1097/00003246-199708000-00035.

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5

Fenner, Annette. "6-monthly PSA test predicts survival." Nature Reviews Urology 13, no. 9 (August 2, 2016): 495. http://dx.doi.org/10.1038/nrurol.2016.145.

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6

Park, Sang-Gue, Choon-Mo Ahn, Byung-Chun Kim, and Yong-Goo Lee. "Logrank test for bivariate survival data." Communications in Statistics - Simulation and Computation 29, no. 2 (January 2000): 533–40. http://dx.doi.org/10.1080/03610910008813626.

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7

Baumgartner, János, Zoltán Süle, Botond Bertók, and János Abonyi. "Test-sequence optimisation by survival analysis." Central European Journal of Operations Research 27, no. 2 (August 21, 2018): 357–75. http://dx.doi.org/10.1007/s10100-018-0578-z.

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8

Mills, Laura. "Wilderness Survival Guide to the T Test." Journal of Therapeutic Schools and Programs 1, no. 9 (2017): 54–57. http://dx.doi.org/10.19157/jtsp.issue.09.01.07.

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9

Hart, Stephen. "Test-Tube Survival of the Molecularly Fit." BioScience 43, no. 11 (December 1993): 738–41. http://dx.doi.org/10.2307/1312317.

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10

Commenges, D., and P. K. Andersen. "Score test of homogeneity for survival data." Lifetime Data Analysis 1, no. 2 (1995): 145–56. http://dx.doi.org/10.1007/bf00985764.

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11

Bonetti, Marco, Chiara Gigliarano, and Pietro Muliere. "The Gini concentration test for survival data." Lifetime Data Analysis 15, no. 4 (September 2, 2009): 493–518. http://dx.doi.org/10.1007/s10985-009-9125-5.

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12

Chen, Han, Thomas Lumley, Jennifer Brody, Nancy L. Heard-Costa, Caroline S. Fox, L. Adrienne Cupples, and Josée Dupuis. "Sequence Kernel Association Test for Survival Traits." Genetic Epidemiology 38, no. 3 (January 26, 2014): 191–97. http://dx.doi.org/10.1002/gepi.21791.

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13

Reed, Carroll E. "Pennsylvania German: A Test of Semantic Survival." Yearbook of German-American Studies 20 (December 1, 1985): 133–40. http://dx.doi.org/10.17161/ygas.v20i.19359.

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14

Graves, Thomas S., and James L. Pazdan. "A permutation test analogue to tarone’s test for trend in survival analysis." Journal of Statistical Computation and Simulation 53, no. 1-2 (October 1995): 79–89. http://dx.doi.org/10.1080/00949659508811697.

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15

Deshevaya, Е. А., V. Ya Pecherkin, L. M. Vasilyak, E. V. Shubralova, N. D. Novikova, А. А. Guridov, and O. S. Tsygankov. "SURVIVAL OF MICROORGANISMS ON TEST OBJECTS DURING VACUUMIZATION." Aerospace and Environmental Medicine 52, no. 2 (2018): 54–59. http://dx.doi.org/10.21687/0233-528x-2018-52-2-54-59.

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16

Kruger, Heidi. "Alliance Survival: A Reconceptualization and Test of Constructs." Academy of Management Proceedings 2016, no. 1 (January 2016): 17201. http://dx.doi.org/10.5465/ambpp.2016.17201abstract.

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17

Manukjanová, Alžbta, Táňa Štechová, and Jan Kučera. "Drought Survival Test of Eight Fen Moss Species." Cryptogamie, Bryologie 35, no. 4 (October 2014): 397–403. http://dx.doi.org/10.7872/cryb.v35.iss4.2014.397.

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18

Winship, Christopher. "Heterogeneity and Interdependence: A Test Using Survival Models." Sociological Methodology 16 (1986): 250. http://dx.doi.org/10.2307/270925.

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19

Edelmann, Dominic, Maral Saadati, Hein Putter, and Jelle Goeman. "A global test for competing risks survival analysis." Statistical Methods in Medical Research 29, no. 12 (July 6, 2020): 3666–83. http://dx.doi.org/10.1177/0962280220938402.

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Standard tests for the Cox model, such as the likelihood ratio test or the Wald test, do not perform well in situations, where the number of covariates is substantially higher than the number of observed events. This issue is perpetuated in competing risks settings, where the number of observed occurrences for each event type is usually rather small. Yet, appropriate testing methodology for competing risks survival analysis with few events per variable is missing. In this article, we show how to extend the global test for survival by Goeman et al. to competing risks and multistate models[Per journal style, abstracts should not have reference citations. Therefore, can you kindly delete this reference citation.]. Conducting detailed simulation studies, we show that both for type I error control and for power, the novel test outperforms the likelihood ratio test and the Wald test based on the cause-specific hazards model in settings where the number of events is small compared to the number of covariates. The benefit of the global tests for competing risks survival analysis and multistate models is further demonstrated in real data examples of cancer patients from the European Society for Blood and Marrow Transplantation.
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20

Chen, Zhongxue. "Extension of Mood’s median test for survival data." Statistics & Probability Letters 95 (December 2014): 77–84. http://dx.doi.org/10.1016/j.spl.2014.08.006.

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21

Assane, Cachimo Combo, Basilio de Bragança Pereira, and Carlos Alberto de Bragança Pereira. "Bayesian significance test for discriminating between survival distributions." Communications in Statistics - Theory and Methods 47, no. 24 (December 5, 2017): 6095–107. http://dx.doi.org/10.1080/03610926.2017.1406117.

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22

Rahbar, Mohammad H., Zhongxue Chen, Sangchoon Jeon, Joseph C. Gardiner, and Jing Ning. "A nonparametric test for equality of survival medians." Statistics in Medicine 31, no. 9 (February 3, 2012): 844–54. http://dx.doi.org/10.1002/sim.5309.

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23

Yin, Guosheng, and Donglin Zeng. "Pair Chart Test for an Early Survival Difference." Lifetime Data Analysis 11, no. 1 (March 2005): 117–29. http://dx.doi.org/10.1007/s10985-004-5643-3.

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24

Milius, Susan. "Tough frills: Ferns' wimp stage aces survival test." Science News 172, no. 20 (November 17, 2007): 307–8. http://dx.doi.org/10.1002/scin.2007.5591722003.

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25

KIMELDORF, MARTIN. "The Job Hunt: A Modern Test of Survival." Journal of Employment Counseling 23, no. 1 (March 1986): 31–37. http://dx.doi.org/10.1002/j.2161-1920.1986.tb00190.x.

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26

Rao, S. S. "Ceriodaphnia reticulata seven-day survival and reproduction test." Toxicity Assessment 3, no. 2 (March 1988): 239–44. http://dx.doi.org/10.1002/tox.2540030210.

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27

Edwards, J. E. Hocking, R. M. Gould, and K. J. Copping. "Putting Merino weaner management recommendations to the test." Australian Journal of Experimental Agriculture 48, no. 7 (2008): 974. http://dx.doi.org/10.1071/ea08003.

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Merino weaner survival is poor under normal traditional management practices. Data from four trials conducted between 2001 and 2005 were used to assess the effectiveness of current industry recommendations to achieve 95% survival rates of Merino weaners. There was very little difference between 2001 and 2003 in survival of Merino weaners despite average weaning weight ranging from 20 to 24 kg between years. The recommended target weaning weight of 23 kg does not always appear to be a good predictor of survival. However, a target of 45% mature liveweight at pasture senescence achieved the industry recommendation of 95% weaner survival. Merino weaners were fed over the summer–autumn period to reach 40 kg liveweight by the time green feed became available. Weaner survival reached 93% compared with 89% survival to hogget shearing in weaners managed under normal growth paths. Furthermore, twice as many ewe weaners died in the traditional feeding program compared with the well fed ewe weaners. Differences in mortality between feeding levels in the wether weaners was less than that observed in the ewe weaners. Flock average growth rates were not useful indicators of weaner survival and there was no relationship between Merino ram estimated breeding values for weaning weight and the survival of the progeny.
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28

Wolski, Anna, Nathalie Grafféo, and Roch Giorgi. "A permutation test based on the restricted mean survival time for comparison of net survival distributions in non-proportional excess hazard settings." Statistical Methods in Medical Research 29, no. 6 (August 28, 2019): 1612–23. http://dx.doi.org/10.1177/0962280219870217.

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Net survival is used in epidemiological studies to assess excess mortality due to a given disease when causes of death are unreliable. By correcting for the general population mortality, it allows comparisons between regions or periods and thus evaluation of health policies. The Pohar-Perme non-parametric estimator of net survival has been recently proposed, soon followed by an appropriate log-rank-type test. However, log-rank tests are known to be under-optimal in non-proportional settings (e.g. crossing of the hazard functions). In classical survival analysis, one solution is to compare the restricted mean survival times. A difference in restricted mean survival time represents a life benefit or loss over the studied period. In the present article the restricted mean net survival time was used to derive a specific test statistic to compare net survivals in proportional and non-proportional hazards settings. The new test was generalized to more than two groups and to stratified analysis. The test performance was assessed on simulation study, compared to the log-rank-type test, and its use illustrated on a population-based colorectal cancer registry. The new test for net survival comparisons proved robust to non-proportionality and well-performing in proportional hazards situations. Furthermore, it is also suited to the classical survival framework.
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29

Shen, Yu, and Thomas R. Fleming. "Weighted Mean Survival Test Statistics: a Class of Distance Tests for Censored Survival Data." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 59, no. 1 (1997): 269–80. http://dx.doi.org/10.1111/1467-9868.00068.

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30

O'Neill, Terence J. "A Test of Equality of Survival Distributions for Two Sample Censored Grouped Survival Data." Biometrical Journal 27, no. 6 (1985): 697–705. http://dx.doi.org/10.1002/bimj.4710270616.

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31

CHECK, M. L., K. SWENSON, J. H. CHECK, D. KIEFER, and D. SUMMERS-CHASE. "SPERM TOXICITY EVALUATED BY THE OVERNIGHT SPERM SURVIVAL TEST." Archives of Andrology 49, no. 2 (January 2003): 161–64. http://dx.doi.org/10.1080/01485010390129287.

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32

Kanjo, Anis I. "An exact test for nbue class of survival functions." Communications in Statistics - Theory and Methods 22, no. 3 (January 1993): 787–95. http://dx.doi.org/10.1080/03610929308831056.

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33

Check, M. L., K. Swenson, J. H. Check, D. Kiefer, and D. Summers-Chase. "SPERM TOXICITY EVALUATED BY THE OVERNIGHT SPERM SURVIVAL TEST." Archives of Andrology 49, no. 2 (March 2003): 161–64. http://dx.doi.org/10.1080/713828096.

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34

Bandinelli, Stefania, Yuri Milaneschi, and Luigi Ferrucci. "Chair Stands Test and Survival in the Older Population." Journal of the American Geriatrics Society 57, no. 11 (November 2009): 2172–73. http://dx.doi.org/10.1111/j.1532-5415.2009.02544.x.

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35

Pons, O., and M. Lemdani. "Estimation and test in long-term survival mixture models." Computational Statistics & Data Analysis 41, no. 3-4 (January 2003): 465–79. http://dx.doi.org/10.1016/s0167-9473(02)00178-0.

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36

Jeong, Gyu-Jin. "A Covariate-adjusted Logrank Test for Paired Survival Data." Communications for Statistical Applications and Methods 9, no. 2 (August 1, 2002): 533–42. http://dx.doi.org/10.5351/ckss.2002.9.2.533.

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37

Munetsugu, Tetsuya, and Takashi Horiuchi. "Influences of survival processing and delay on recognition test." Proceedings of the Annual Convention of the Japanese Psychological Association 77 (September 19, 2013): 3AM—116–3AM—116. http://dx.doi.org/10.4992/pacjpa.77.0_3am-116.

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38

Balakrishnan, Kirushanthini, and M. R. Sooriyarachchi. "A goodness of fit test for multilevel survival data." Communications in Statistics - Simulation and Computation 47, no. 1 (October 18, 2017): 30–47. http://dx.doi.org/10.1080/03610918.2016.1186184.

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39

Arrigoni, Arrigo, Tiziana Gindro, Giuseppe Aimo, Nazario Cappello, Andrea Meloni, Paolo Benedetti, Gian Paolo Molino, Giorgio Verme, and Mario Rizzetto. "Monoethylglicinexylidide test: A prognostic indicator of survival in cirrhosis." Hepatology 20, no. 2 (August 1994): 383–87. http://dx.doi.org/10.1002/hep.1840200218.

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40

Huang, Xuelin, Robert A. Wolfe, and Chengcheng Hu. "A test for informative censoring in clustered survival data." Statistics in Medicine 23, no. 13 (June 15, 2004): 2089–107. http://dx.doi.org/10.1002/sim.1801.

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41

Liu, Tiantian, Marc Ditzhaus, and Jin Xu. "A resampling‐based test for two crossing survival curves." Pharmaceutical Statistics 19, no. 4 (January 8, 2020): 399–409. http://dx.doi.org/10.1002/pst.2000.

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42

Péron, Julien, Alexandre Lambert, Stephane Munier, Brice Ozenne, Joris Giai, Pascal Roy, Stéphane Dalle, Abigirl Machingura, Delphine Maucort-Boulch, and Marc Buyse. "Assessing Long-Term Survival Benefits of Immune Checkpoint Inhibitors Using the Net Survival Benefit." JNCI: Journal of the National Cancer Institute 111, no. 11 (March 5, 2019): 1186–91. http://dx.doi.org/10.1093/jnci/djz030.

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Abstract Background The treatment effect in survival analysis is commonly quantified as the hazard ratio, and tested statistically using the standard log-rank test. Modern anticancer immunotherapies are successful in a proportion of patients who remain alive even after a long-term follow-up. This new phenomenon induces a nonproportionality of the underlying hazards of death. Methods The properties of the net survival benefit were illustrated using the dataset from a trial evaluating ipilimumab in metastatic melanoma. The net survival benefit was then investigated through simulated datasets under typical scenarios of proportional hazards, delayed treatment effect, and cure rate. The net survival benefit test was computed according to the value of the minimal survival difference considered clinically relevant. As comparators, the standard and the weighted log-rank tests were also performed. Results In the illustrative dataset, the net survival benefit favored ipilimumab [Δ(0) = 15.8%, 95% confidence interval = 4.6% to 27.3%, P = .006]. This favorable effect was maintained when the analysis was focused on long-term survival differences (eg, >12 months, Δ(12) = 12.5% (95% confidence interval = 4.4% to 20.6%, P = .002). Under the scenarios of a delayed treatment effect and cure rate, the power of the net survival benefit test compared favorably to the standard log-rank test power and was comparable to the power of the weighted log-rank test for large values of the threshold of clinical relevance. Conclusion The net long-term survival benefit is a measure of treatment effect that is meaningful whether or not hazards are proportional. The associated statistical test is more powerful than the standard log-rank test when a delayed treatment effect is anticipated.
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43

Agrawal, Shree, Jason A. Efstathiou, Jeff M. Michalski, Thomas Michael Pisansky, Bridget F. Koontz, Stanley L. Liauw, Matthew Abramowitz, et al. "Prostate cancer specific mortality and overall survival outcomes for salvage radiation therapy after radical prostatectomy." Journal of Clinical Oncology 2017, no. 1_suppl (February 20, 2017): 9. http://dx.doi.org/10.1200/jco.2017.35.6_suppl.9.2017.1.test.

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44

Bailer, John A., and James T. Oris. "Implications of defining test acceptability in terms of control-group survival in two-group survival studies." Environmental Toxicology and Chemistry 15, no. 7 (July 1996): 1242–44. http://dx.doi.org/10.1002/etc.5620150732.

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45

Lee, Derek E., Monica L. Bond, Bernard M. Kissui, Yustina A. Kiwango, and Douglas T. Bolger. "Spatial variation in giraffe demography: a test of 2 paradigms." Journal of Mammalogy 97, no. 4 (May 11, 2016): 1015–25. http://dx.doi.org/10.1093/jmammal/gyw086.

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Abstract Examination of spatial variation in demography among or within populations of the same species is a topic of growing interest in ecology. We examined whether spatial variation in demography of a tropical megaherbivore followed the “temporal paradigm” or the “adult survival paradigm” of ungulate population dynamics formulated from temperate-zone studies. We quantified spatial variation in demographic rates for giraffes (Giraffa camelopardalis) at regional and continental scales. Regionally, we used photographic capture-mark-recapture data from 860 adult females and 449 calves to estimate adult female survival, calf survival, and reproduction at 5 sites in the Tarangire ecosystem of Tanzania. We examined potential mechanisms for spatial variation in regional demographic rates. At the continental scale, we synthesized demographic estimates from published studies across the range of the species. We created matrix population models for all sites at both scales and used prospective and retrospective analyses to determine which vital rate was most important to variation in population growth rate. Spatial variability of demographic parameters at the continental scale was in agreement with the temporal paradigm of low variability in adult survival and more highly variable reproduction and calf survival. In contrast, at the regional scale, adult female survival had higher spatial variation, in agreement with the adult survival paradigm. At both scales, variation in adult female survival made the greatest contribution to variation in local population growth rates. Our work documented contrasting patterns of spatial variation in demographic rates of giraffes at 2 spatial scales, but at both scales, we found the same vital rate was most important. We also found anthropogenic impacts on adult females are the most likely mechanism of regional population trajectories.
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46

Lee, Seung-Hwan, and Eun-Joo Lee. "Modifying Weighted Kaplan-Meier Test for Two-Sample Survival Comparison." European Journal of Mathematics and Statistics 3, no. 1 (February 20, 2022): 51–58. http://dx.doi.org/10.24018/ejmath.2022.3.1.93.

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This paper presents an approach to improving the weighted Kaplan-Meier test statistics in order to make it a more useful tool for a long-term comparison of two underlying survival distributions in the presence of right-censored data. The procedures are based on the use of some weight function that involves the percentage of censored data as a component. Some versatile procedures for the alternative, not pre-specified, are also discussed. Numerical simulations are conducted to investigate the performance of the proposed procedures. For illustration, the procedures are applied to real-world data in clinical trials, where patients with tongue cancer are divided into two groups according to tumor DNA.
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47

Healy, Pat. "Call for trial into HPV test and cancer survival rates." Nursing Standard 14, no. 49 (August 23, 2000): 8. http://dx.doi.org/10.7748/ns.14.49.8.s20.

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48

Heller, Glenn, and E. S. Venkatraman. "A nonparametric test to compare survival distributions with covariate adjustment." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 66, no. 3 (August 2004): 719–33. http://dx.doi.org/10.1111/j.1467-9868.2004.b5364.x.

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49

Grafféo, Nathalie, Fabienne Castell, Aurélien Belot, and Roch Giorgi. "A log-rank-type test to compare net survival distributions." Biometrics 72, no. 3 (January 28, 2016): 760–69. http://dx.doi.org/10.1111/biom.12477.

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50

Myers, Jonathan, Odd Geiran, Svein Simonsen, Afshin Ghuyoumi, and Lars Gullestad. "Clinical and Exercise Test Determinants of Survival After Cardiac Transplantation *." Chest 124, no. 5 (November 2003): 2000–2005. http://dx.doi.org/10.1378/chest.124.5.2000.

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