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Relevant bibliographies by topics / Survival analysis / Books

Books 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

O'Quigley, John. Survival Analysis. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-33439-0.

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Liu, Xian. Survival Analysis. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118307656.

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3

Machin, David, Yin Bun Cheung, and Mahesh KB Parmar. Survival Analysis. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470034572.

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4

Kleinbaum, David G., and Mitchel Klein. Survival Analysis. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-6646-9.

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5

Klein, John P., and Melvin L. Moeschberger. Survival Analysis. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/b97377.

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6

Kleinbaum, David G. Survival Analysis. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2555-1.

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7

Klein, John P., and Melvin L. Moeschberger. Survival Analysis. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2728-9.

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8

Kleinbaum, David G., and Mitchel Klein. Survival Analysis. New York, NY: Springer New York, 2005. http://dx.doi.org/10.1007/0-387-29150-4.

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9

Guo, Shenyang. Survival analysis. New York: Oxford University Press, 2010.

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10

Guo, Shenyang. Survival analysis. New York: Oxford University Press, 2010.

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11

Hosmer, David W., Stanley Lemeshow, and Susanne May. Applied Survival Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2008. http://dx.doi.org/10.1002/9780470258019.

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12

Ibrahim, Joseph G., Ming-Hui Chen, and Debajyoti Sinha. Bayesian Survival Analysis. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3447-8.

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13

1961-, Chen Ming-Hui, and Sinha Debajyoti, eds. Bayesian survival analysis. New York: Springer, 2001.

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14

Karim, Md Rezaul, and M. Ataharul Islam. Reliability and Survival Analysis. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9776-9.

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15

1956-, Balakrishnan N., and Rao C. Radhakrishna 1920-, eds. Advances in survival analysis. Amsterdam: Elsevier North-Holland, 2004.

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16

Cox, D. R. Analysis of survival data. London: Chapman and Hall, 1990.

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17

Liu, Xian. Survival analysis: Models and applications. Chichester, West Sussex: John Wiley & Sons, 2012.

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18

Allison, Paul. Event History and Survival Analysis. 2455 Teller Road, Thousand Oaks California 91320 United States: SAGE Publications, Inc., 2014. http://dx.doi.org/10.4135/9781452270029.

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19

Hougaard, Philip. Analysis of Multivariate Survival Data. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1304-8.

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20

Aalen, Odd O., Ørnulf Borgan, and Håkon K. Gjessing. Survival and Event History Analysis. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-68560-1.

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21

Emura, Takeshi, Shigeyuki Matsui, and Virginie Rondeau. Survival Analysis with Correlated Endpoints. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3516-7.

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22

Moore, Dirk F. Applied Survival Analysis Using R. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31245-3.

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23

Elandt-Johnson, Regina C., and Norman L. Johnson. Survival Models and Data Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1999. http://dx.doi.org/10.1002/9781119011040.

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24

Fleming, Thomas R., and David P. Harrington. Counting Processes and Survival Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/9781118150672.

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25

Analysis of multivariate survival data. New York: Springer, 2000.

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26

Elandt-Johnson, Regina C. Survival models and data analysis. New York: Wiley, 1999.

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27

Liu, Xian. Survival analysis: Models and applications. Chichester, West Sussex: John Wiley & Sons, 2012.

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28

Kragh, Andersen Per, and Keiding Niels, eds. Survival and event history analysis. Chichester, West Sussex, England: Wiley, 2006.

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29

Matthews, Stephen A. Computer packages for survival analysis. Cardiff: Wales and South West Regional Research Laboratory, 1989.

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30

Bun, Cheung Yin, Parmar Mahesh K. B, and Parmar Mahesh K. B, eds. Survival analysis: A practical approach. 2nd ed. Chichester, West Sussex, England: Wiley, 2006.

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31

P, Harrington David, ed. Counting processes and survival analysis. New York: Wiley, 1991.

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32

David, Machin, ed. Survival analysis: A practical approach. Chichester: J. Wiley, 1995.

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33

Tattar, Prabhanjan, and H. J. Vaman. Survival Analysis. Taylor & Francis Group, 2022.

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34

Liu, Xian. Survival Analysis. Wiley & Sons, Incorporated, John, 2012.

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35

Vaman, H. J., and Prabhanjan Tattar. Survival Analysis. CRC Press, 2022.

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36

Peacock, Janet L., Sally M. Kerry, and Raymond R. Balise. Survival analysis. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198779100.003.0011.

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Chapter 11 covers survival analysis, and includes Kaplan–Meier estimates, and the logrank test. Cox regression is used to do multifactorial analyses with results reported as adjusted hazard ratios. The chapter includes analyses using Stata, SAS, SPSS, and R.

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37

Peacock, Janet L., and Sally M. Kerry. Survival analysis. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780198599661.003.0011.

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38

Golub, Jonathan. Survival Analysis. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0023.

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This article provides a discussion of survival analysis that presents another way to incorporate temporal information into analysis in ways that give advantages similar to those from using time series. It describes the main choices researchers face when conducting survival analysis and offers a set of methodological steps that should become standard practice. After introducing the basic terminology, it shows that there is little to lose and much to gain by employing Cox models instead of parametric models. Cox models are superior to parametric models in three main respects: they provide more reliable treatment of the baseline hazard and superior handling of the proportional hazards assumption, and they are the best for handling tied data. Moreover, the illusory benefits of parametric models are presented. The greater use of Cox models enables researchers to elicit more useful information from their data, and allows for more reliable substantive inferences about important political processes.

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39

Watson, Peter. Survival analysis. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0018.

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This chapter explores survival analysis. It includes data censoring, functions of duration time (the survival function, and hazard function), Cox’s proportional hazards model, log-linearity, time varying predictors, and odds ratios.

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40

Tattar, Prabhanjan, and H. J. Vaman. Survival Analysis. CRC Press LLC, 2022.

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41

Survival Analysis. Wiley & Sons, Incorporated, John, 2011.

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42

Survival Analysis. Wiley-Interscience, 1998.

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43

Jr, Miller Rupert G. Survival Analysis. Wiley & Sons, Incorporated, John, 2011.

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44

Moeschberger, Melvin L., and John P. Klein. Survival Analysis. Springer, 2005.

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45

Survival analysis. New York: Oxford University Press, 2010.

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46

Machin, David, Yin Bun Cheung, and Mahesh Parmar. Survival Analysis. Wiley & Sons, Incorporated, John, 2006.

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47

Miller, Rupert G. Survival Analysis. John Wiley & Sons Inc, 1997.

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48

Tattar, Prabhanjan, and H. J. Vaman. Survival Analysis. Taylor & Francis Group, 2022.

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49

Tattar, Prabhanjan, and H. J. Vaman. Survival Analysis. Taylor & Francis Group, 2022.

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50

Guo, Shenyang. Survival Analysis. Oxford University Press, 2009.

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