The Statistical Analysis of Failure Time Data: Kalbfleisch/The Statistical

By John D. Kalbfleisch, R. Prentice. 2002

2,431 citations recorded by the source corpus.

Subfields: Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/b3501b8b16f78dc4b306f351bba176842e83a6f9

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Citations in the corpus

Human life is unlimited – but short (2017, background): “…truncated and/or censored observations, such as the truncated observations in the IDL database, is of central interest in survival analysis and in analysis of failure time data, see e.g. Andersen et al. (1993) and Kalbfleisch and Prentice (2002) for much more information and asymptotic theory.”
Organizational Forms and Multi-population Dynamics (2014, methodology): “…parametrizations of waiting time (Ingram and Baum, 1997), we chose to apply Cox’s proportional hazard model, which estimates the influence of covariates on the hazard of an exit event without specifying a parametric form of distribution (Cox and Oakes, 1984; Kalbfleisch and Prentice, 2002).”
What can Statistics Contribute to a Causal Understanding? (2007, background): “The recent revised version of the classical book by Kalbfleisch & Prentice (2002) contains discussion on this point, but no solution is presented.”
Maximum likelihood estimation in semiparametric regression models with censored data (2007, methodology): “Multivariate or dependent failure time data arise when each study subject can potentially experience several events or when subjects are sampled in clusters (Kalbfleisch and Prentice (2002), chapters 8–10).”
Choice Probability Generating Functions (2012, methodology): “The CDF R is not identified in general without further assumptions (Kalbfleisch and Prentice, 2002).”
A Review of Methods to Estimate Cause-Specific Mortality in Presence of Competing Risks (2006, background): “In biostatistical survival analysis, researchers call this relationship the cumulative incidence function (CIF; Kalbfleisch and Prentice 2002).”
Partially linear hazard regression with varying coefficients for multivariate survival data (2008, methodology): “Vlj, β/TZlj} ]) : .2:4/ Here and hereafter, for ease of presentation, we sometimes drop the dependence of covariates on time, with the understanding that the methods that are developed in this paper are applicable to external time-dependent covariates (Kalbfleisch and Prentice, 2002).”
Time allocation strategies in insect parasitoids: from ultimate predictions to proximate behavioral mechanisms (2006, methodology): “In this case, so-called survival analyses can be used (Collett 1994; Kalbfleisch and Prentice 2002).”
Generalization of the Weibull distribution: the odd Weibull family (2006, neutral): “…(xj θ )α + (β − 1) ln (e(xj/θ)α − 1)} − n∑ j=1 (1 + δj) ln ( 1 + ( e(xj/θ) α − 1 )β) (3.1) where δj = { 0 if jth observation is right censored, j = 1, 2, . . . , n 1 else The likelihood procedures are available in Cox and Oakes (1984), Kalbfleisch and Prentice (2002), Rao (1973) and Lawless (2003).”
Institutions and the internationalization of US venture capital firms (2010, background): “0 Pr tpTot þ DtjTXtf g Dt ð1Þ and specifies the instantaneous rate at which entry occurs at time t, given that the event has not yet occurred (Allison, 1995; Kalbfleisch & Prentice, 2002).”
Cumulative Incidence Estimation in the Presence of Competing Risks (2004, methodology): “Extending the dataset above, the example (Kalbfleisch and Prentice 2002, 252, example 8.1) is now reproduced to illustrate a further point related to the cumulative incidence.”