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Books on the topic 'Survival data analysi'

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Published: 9 March 2023

Last updated: 10 March 2023

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1

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

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2

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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3

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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4

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

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5

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

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6

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

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7

Statistical methods for survival data analysis. 2nd ed. New York: Wiley, 1992.

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8

Lee, Elisa T., and John Wenyu Wang. Statistical Methods for Survival Data Analysis. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2003. http://dx.doi.org/10.1002/0471458546.

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9

Wenyu, Wang John, ed. Statistical methods for survival data analysis. 3rd ed. New York: J. Wiley, 2003.

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10

Murphy, Roberta. Survival analysis of nurse manpower data. [S.l: The Author], 1997.

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11

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

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12

Hanagal, David D. Modeling survival data using frailty models. Boca Raton, FL: Chapman & Hall/CRC, 2011.

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13

Emura, Takeshi, and Yi-Hau Chen. Analysis of Survival Data with Dependent Censoring. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7164-5.

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14

Modelling survival data in medical research. London: Chapman & Hall, 1994.

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15

Modelling survival data in medical research. 2nd ed. Boca Raton, Fla: Chapman & Hall/CRC, 2003.

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16

1952-, Jewell Nicholas P., ed. Lifetime data: Models in reliability and survival analysis. Dordrecht: Kluwer Academic, 1996.

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17

Tableman, Mara. Survival analysis using S: Analysis of time-to-event data. Boca Raton, FL: Chapman & Hall/CRC, 2002.

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18

Sung, Kim Jong, ed. Survival analysis using S: Analysis of time-to-event data. Boca Raton, Fla: Chapman & Hall/CRC, 2004.

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19

L, Moeschberger Melvin, ed. Survival analysis: Techniques for censored and truncated data. New York: Springer, 1997.

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20

L, Moeschberger Melvin, ed. Survival analysis: Techniques for censored and truncated data. 2nd ed. New York: Springer, 2003.

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21

Jewell, Nicholas P., Alan C. Kimber, Mei-Ling Ting Lee, and G. A. Whitmore, eds. Lifetime Data: Models in Reliability and Survival Analysis. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-5654-8.

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22

Nondetects and data analysis: Statistics for censored environmental data. Hoboken, N.J: Wiley-Interscience, 2005.

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23

Clinical statistics: Introducing clinical trials, survival analysis, and longitudinal data analysis. Sudbury, Mass: Jones and Bartlett Publishers, 2009.

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24

Grazia, Valsecchi Maria, ed. Analysing survival data from clinical trials and observational studies. Chichester: J. Wiley, 1995.

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25

Stanley, Lemeshow, ed. Applied survival analysis: Regression modeling of time to event data. New York: Wiley, 1999.

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26

NATO Advanced Research Workshop on Survival Analysis and Related Topics (1991 Columbus, Ohio). Survival analysis: State of the art. Dordrecht: Kluwer Academic Publishers, 1992.

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27

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

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28

Allison, Paul David. Survival analysis using the SAS system: A practical guide. Cary, NC: SAS Institute, 1995.

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29

Alan, Cantor, ed. SAS survival analysis techniques for medical research. 2nd ed. Cary, NC, USA: SAS Pub., 2003.

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30

Hosmer, David W. Applied survival analysis: Regression modeling of time-to-event data. 2nd ed. Hoboken, N.J: Wiley-Interscience, 2008.

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31

Hosmer, David W. Applied survival analysis: Regression modeling of time-to-event data. 2nd ed. Hoboken, N.J: John Wiley & Sons, 2008.

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32

Lemeshow, Stanley. Applied Survival Analysis: Regression Modeling of Time to Event Data. 2nd ed. Hoboken, N.J: Wiley-Interscience, 2008.

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33

Cox, D. R. Analysis of Survival Data. Taylor & Francis Group, 2018.

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34

Cox, D. R. Analysis of Survival Data. Taylor & Francis Group, 2018.

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35

Cox, D. R. Analysis of Survival Data. Taylor & Francis Group, 2018.

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36

Cox, D. R. Analysis of Survival Data. Taylor & Francis Group, 2018.

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37

SEA. Survival Moels & Data Analaysis. John Wiley & Sons Inc, 1994.

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38

Lesaffre, Emmanuel, Kris Bogaerts, and Arnost Komárek. Survival Analysis with Interval-Censored Data. Taylor & Francis Group, 2020.

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39

Johnson, Norman L., and Regina C. Elandt-Johnson. Survival Models and Data Analysis. Wiley & Sons, Limited, John, 2014.

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40

Hougaard, Philip. Analysis of Multivariate Survival Data. Springer, 2012.

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41

Johnson, Norman L., and Regina C. Elandt-Johnson. Survival Models and Data Analysis. Wiley & Sons, Incorporated, John, 2014.

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42

Hougaard, Philip. Analysis of Multivariate Survival Data. Springer New York, 2012.

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43

Hougaard, Philip. Analysis of Multivariate Survival Data. Springer, 2001.

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44

Johnson, Norman L., and Regina C. Elandt-Johnson. Survival Models and Data Analysis. Wiley & Sons, Incorporated, John, 2014.

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45

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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46

Analysis of Failure and Survival Data. Chapman & Hall/CRC, 2002.

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47

Martinussen, Torben, and Thomas H. Scheike. Dynamic Regression Models for Survival Data. Springer New York, 2010.

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48

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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Abstract:

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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49

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

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50

Ibrahim, Joseph G., Ming-Hui Chen, and Debajyoti Sinha. Bayesian Survival Analysis. Springer, 2004.

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