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Journal articles on the topic 'Survival analysis'

To see the other types of publications on this topic, follow the link: Survival analysis.

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2024

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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 (April 5, 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 (April 1, 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 (July 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 (February 1997): 112. http://dx.doi.org/10.2307/1270802.

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5

Kim, Julia. "Survival Analysis." Pediatrics In Review 33, no. 4 (April 1, 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 (February 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 (August 4, 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 (August 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 (May 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 (October 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 (October 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 (June 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 (July 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 (December 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 (May 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 (March 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 (October 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 (May 1, 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 (March 30, 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 (June 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 (September 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 (December 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 (June 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 (April 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 (April 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 (January 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 (January 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 (August 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 (February 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 (November 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 (November 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 (November 13, 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 (June 1, 2023): e14695-e14695. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e14695.

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Abstract:

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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35

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

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36

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

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37

Tahar, Ait Mouheb, Merrouche Brahim, Ait Mokhtar Lynda, Amine Zakaria, Slimani Mohamed, Feriel Amrou, and Labaci Fatima. "Predicting mortality in patient with ischemic stroke: a survival analysis." International Journal of Research Publication and Reviews 5, no. 5 (May 7, 2024): 2773–78. http://dx.doi.org/10.55248/gengpi.5.0524.1211.

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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 (August 26, 2003): 781–86. http://dx.doi.org/10.1038/sj.bjc.6601117.

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39

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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40

Ren, Kan, Jiarui Qin, Lei Zheng, Zhengyu Yang, Weinan Zhang, Lin Qiu, and Yong Yu. "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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Abstract:

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 patterns within the feature space. In this paper, we propose a Deep Recurrent Survival Analysis model which combines deep learning for conditional probability prediction at finegrained level of the data, and survival analysis for tackling the censorship. By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i.e., the probability of the non-occurrence of the event, for the censored data. Meanwhile, without assuming any specific form of the event probability distribution, our model shows great advantages over the previous works on fitting various sophisticated data distributions. In the experiments on the three realworld tasks from different fields, our model significantly outperforms the state-of-the-art solutions under various metrics.

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41

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

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42

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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43

Pugh, Stephanie L. "Essence of survival analysis†." Neuro-Oncology Practice 4, no. 2 (December 9, 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-an-event data, compare the rates between groups, and assess how other factors impact these rates.

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44

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

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45

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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46

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

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47

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

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48

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

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49

Jayet, H., and A. Moreau. "Analysis of survival data." Journal of Econometrics 48, no. 1-2 (April 1991): 263–85. http://dx.doi.org/10.1016/0304-4076(91)90041-b.

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50

Oakes, D. "Biometrika Centenary: Survival analysis." Biometrika 88, no. 1 (February 1, 2001): 99–142. http://dx.doi.org/10.1093/biomet/88.1.99.

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