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Journal articles on the topic 'Survival analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

V Kannan R, Pavithra. "Impact of Influential Observations in Survival Analysis." International Journal of Science and Research (IJSR) 12, no. 4 (2023): 1057–62. http://dx.doi.org/10.21275/mr23410105218.

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2

Kim, J. "Survival Analysis." Pediatrics in Review 33, no. 4 (2012): 172–74. http://dx.doi.org/10.1542/pir.33-4-172.

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3

Stolberg, Harald O., Geoffrey Norman, and Isabelle Trop. "Survival Analysis." American Journal of Roentgenology 185, no. 1 (2005): 19–22. http://dx.doi.org/10.2214/ajr.185.1.01850019.

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4

Ziegel, Eric R., M. Parmar, and D. Machin. "Survival Analysis." Technometrics 39, no. 1 (1997): 112. http://dx.doi.org/10.2307/1270802.

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5

Kim, Julia. "Survival Analysis." Pediatrics In Review 33, no. 4 (2012): 172–74. http://dx.doi.org/10.1542/pir.33.4.172.

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6

Ravishanker, Nalini. "Survival Analysis." Technometrics 46, no. 1 (2004): 111–12. http://dx.doi.org/10.1198/tech.2004.s740.

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7

Flynn, Robert. "Survival analysis." Journal of Clinical Nursing 21, no. 19pt20 (2012): 2789–97. http://dx.doi.org/10.1111/j.1365-2702.2011.04023.x.

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8

Miller, Rupert G. "Survival Analysis." IEEE Transactions on Reliability 35, no. 3 (1986): 315. http://dx.doi.org/10.1109/tr.1986.4335441.

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9

Freedman, David A. "Survival Analysis." American Statistician 62, no. 2 (2008): 110–19. http://dx.doi.org/10.1198/000313008x298439.

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10

Streiner, David L., and Geoffrey R. Norman. "Survival analysis." Community Oncology 7, no. 10 (2010): 477–79. http://dx.doi.org/10.1016/s1548-5315(11)70429-6.

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11

Kosorok, Michael R., and Chin-Yu Lin. "survival analysis." Annals of Statistics 27, no. 5 (1999): 1722–44. http://dx.doi.org/10.1214/aos/1017939149.

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12

Blackwell, Elizabeth, and Marcello Pagano. "Survival analysis." Nutrition 12, no. 6 (1996): 459–60. http://dx.doi.org/10.1016/s0899-9007(96)00183-9.

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13

Kartsonaki, Christiana. "Survival analysis." Diagnostic Histopathology 22, no. 7 (2016): 263–70. http://dx.doi.org/10.1016/j.mpdhp.2016.06.005.

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14

Sylvester, Richard J. "Survival analysis." Controlled Clinical Trials 6, no. 4 (1985): 323–24. http://dx.doi.org/10.1016/0197-2456(85)90110-2.

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15

Scuderi, GR, and JN Insall. "Survival analysis." Journal of Bone and Joint Surgery. British volume 76-B, no. 3 (1994): 507–8. http://dx.doi.org/10.1302/0301-620x.76b3.8175877.

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16

Oakes, David. "Survival Analysis." Journal of the American Statistical Association 95, no. 449 (2000): 282–85. http://dx.doi.org/10.1080/01621459.2000.10473923.

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17

Reznick, Richard K., and Cameron B. Guest. "Survival analysis." Diseases of the Colon & Rectum 32, no. 10 (1989): 898–902. http://dx.doi.org/10.1007/bf02554566.

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18

Shott, S. "Survival analysis." Journal of the American Veterinary Medical Association 198, no. 9 (1991): 1513–15. http://dx.doi.org/10.2460/javma.1991.198.09.1513.

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19

Ferrandis, Eduardo, and Pilar Hernández. "Direct Survival Analysis: a new stock assessment method." Scientia Marina 71, no. 1 (2007): 175–85. http://dx.doi.org/10.3989/scimar.2007.71n1175.

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20

Sarmukaddam, Sanjeev. "Revisiting survival analysis." International Journal of Ayurveda Research 2, no. 1 (2011): 62. http://dx.doi.org/10.4103/0974-7788.83178.

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21

Le, C. T. "Applied Survival Analysis." Biometrics 54, no. 2 (1998): 798. http://dx.doi.org/10.2307/3109796.

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22

Wassell, James T., and Chap T. Le. "Applied Survival Analysis." Journal of the American Statistical Association 93, no. 443 (1998): 1241. http://dx.doi.org/10.2307/2669878.

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23

Yan, Jun. "Bayesian Survival Analysis." Journal of the American Statistical Association 99, no. 468 (2004): 1202–3. http://dx.doi.org/10.1198/jasa.2004.s359.

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24

Cantor, Alan B. "Understanding survival analysis." Evidence-based Oncology 1, no. 2 (2000): 38–40. http://dx.doi.org/10.1054/ebon.2000.0028.

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25

May, Margaret. "Bayesian Survival Analysis." International Journal of Epidemiology 31, no. 2 (2002): 505. http://dx.doi.org/10.1093/intjepid/31.2.505.

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26

May, Margaret. "Bayesian Survival Analysis." International Journal of Epidemiology 31, no. 2 (2002): 505. http://dx.doi.org/10.1093/ije/31.2.505.

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27

Gill, R. D. "Multivariate Survival Analysis." Theory of Probability & Its Applications 37, no. 1 (1993): 18–31. http://dx.doi.org/10.1137/1137005.

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28

Gill, R. D. "Multivariate Survival Analysis." Theory of Probability & Its Applications 37, no. 2 (1993): 284–301. http://dx.doi.org/10.1137/1137061.

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29

Goldberg, Judith D. "Applied Survival Analysis." Technometrics 41, no. 3 (1999): 267–68. http://dx.doi.org/10.1080/00401706.1999.10485684.

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30

Collett, Dave. "Applied Survival Analysis." Controlled Clinical Trials 21, no. 1 (2000): 56–58. http://dx.doi.org/10.1016/s0197-2456(99)00041-0.

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31

Akritas, Michael G. "Nonparametric Survival Analysis." Statistical Science 19, no. 4 (2004): 615–23. http://dx.doi.org/10.1214/088342304000000530.

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32

Szydlo, RM. "Surviving survival analysis." Bone Marrow Transplantation 28, no. 10 (2001): 907. http://dx.doi.org/10.1038/sj.bmt.1703261.

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33

Lansky, Jan. "Cryptocurrency Survival Analysis." Journal of Alternative Investments 22, no. 3 (2019): 55–64. http://dx.doi.org/10.3905/jai.2019.1.084.

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34

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

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

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35

Baada, Ingrid A. "Survival analysis diagnostics." Journal and proceedings of the Royal Society of New South Wales 131, no. 3-4 (1998): 113. http://dx.doi.org/10.5962/p.361426.

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36

Lazarus, Muchabaiwa. "Sex Differentials in Child Survival in Zimbabwe." International Journal of Management Sciences and Business Research 6, no. 6 (2017): 25–47. https://doi.org/10.5281/zenodo.3470789.

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Health strategies which consider sex and gender elements have been noted to be cost effective and more successful than those which do not. The main objective of this paper is to investigate whether an observed widening of the sex differential in child mortality in Zimbabwe represents an avoidable and inequitable bias against the male child. Other objectives include examining the sex differentials in child mortality and also to examine determinants of child survival within the proximate determinants framework. The Zimbabwe Demographic Health Survey kids recode dataset collected between 2010 and

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37

Barlow, Richard E., and Fabio Spizzichino. "Schur-concave survival functions and survival analysis." Journal of Computational and Applied Mathematics 46, no. 3 (1993): 437–47. http://dx.doi.org/10.1016/0377-0427(93)90039-e.

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38

Clark, T. G., M. J. Bradburn, S. B. Love, and D. G. Altman. "Survival Analysis Part IV: Further concepts and methods in survival analysis." British Journal of Cancer 89, no. 5 (2003): 781–86. http://dx.doi.org/10.1038/sj.bjc.6601117.

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39

Tahar, Ait Mouheb, Merrouche Brahim, Ait Mokhtar Lynda, et al. "Predicting mortality in patient with ischemic stroke: a survival analysis." International Journal of Research Publication and Reviews 5, no. 5 (2024): 2773–78. http://dx.doi.org/10.55248/gengpi.5.0524.1211.

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40

Efri, Tri Ardianto, Basuki Notobroto Hari, and Purnomo Windhu. "Survival Analysis of Hemodialysis Patients." International Journal of Public Health Science (IJPHS) 5, no. 3 (2016): 306–12. https://doi.org/10.11591/ijphs.v5i3.4800.

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Survival analysis as a collection of statistical procedures for analyzing the data that its outcome variable was the time to occurrence of an event. Kaplan-Meier method is a type of survival analysis technique, this method is often called the Product Limit Method. Chronic Kidney Disease (CKD) became one of the public health problem throughout the world, including Indonesia. The number of hemodialysis patients has increased every year and have an impact on increasing the number of death in General Hospital IbnuSina Gresik. This study was determine the survival of hemodialysis patients using Kap

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41

Asakura, Koko, and Toshimitsu Hamasaki. "Analysis of survival data." Drug Delivery System 30, no. 5 (2015): 474–84. http://dx.doi.org/10.2745/dds.30.474.

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42

Ren, Kan, Jiarui Qin, Lei Zheng, et al. "Deep Recurrent Survival Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4798–805. http://dx.doi.org/10.1609/aaai.v33i01.33014798.

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Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging from traditional statistic methods to machine learning models. However, the existing methodologies either utilize counting-based statistics on the segmented data, or have a pre-assumption on the event probability distribution w.r.t. time. Moreover, few works consider sequential p

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43

Breslow, N., D. R. Cox, and D. Oakes. "Analysis of Survival Data." Biometrics 41, no. 2 (1985): 593. http://dx.doi.org/10.2307/2530888.

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44

Kachman, Stephen D. "Applicaitons in survival analysis." Journal of Animal Science 77, suppl_2 (1999): 147. http://dx.doi.org/10.2527/1999.77suppl_2147x.

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45

Pugh, Stephanie L. "Essence of survival analysis†." Neuro-Oncology Practice 4, no. 2 (2016): 77–81. http://dx.doi.org/10.1093/nop/npw012.

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AbstractMany clinical trials are designed based on a time-to-event endpoint. Overall survival and progression-free survival are commonly used, especially in Phase II and III clinical trials. Overall survival measures the time to death from any cause, while progression-free survival measures the time to progression of the disease or death from any cause. The key distinguishing factor is that the event of interest, such as death, may not occur in all individuals, making their time to this event unknown. Survival analysis comprises of the methods used to estimate the rates associated with time-to

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46

Berliner, L. Mark, and Bruce M. Hill. "Bayesian Nonparametric Survival Analysis." Journal of the American Statistical Association 83, no. 403 (1988): 772–79. http://dx.doi.org/10.1080/01621459.1988.10478660.

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47

Ziegler, A., S. Lange, and R. Bender. "Survival analysis: Cox regress." DMW - Deutsche Medizinische Wochenschrift 129 (November 2004): T1—T3. http://dx.doi.org/10.1055/s-2004-836074.

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48

Sainani, Kristin L. "Introduction to Survival Analysis." PM&R 8, no. 6 (2016): 580–85. http://dx.doi.org/10.1016/j.pmrj.2016.04.003.

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49

Aletti, Giacomo, and Diane Saada. "Randomization in survival analysis." Statistics & Probability Letters 77, no. 5 (2007): 497–502. http://dx.doi.org/10.1016/j.spl.2006.08.017.

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50

Chung, Ching-Fan, Peter Schmidt, and Ana D. Witte. "Survival analysis: A survey." Journal of Quantitative Criminology 7, no. 1 (1991): 59–98. http://dx.doi.org/10.1007/bf01083132.

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