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Exact and Asymptotic Weighted Logrank Tests for Interval Censored Data: The interval R Package

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  • Fay, Michael P.
  • Shaw, Pamela A.

Abstract

For right-censored data perhaps the most commonly used tests are weighted logrank tests, such as the logrank and Wilcoxon-type tests. In this paper we review several generalizations of those weighted logrank tests to interval-censored data and present an R package, interval, to implement many of them. The interval package depends on the perm package, also presented here, which performs exact and asymptotic linear permutation tests. The perm package performs many of the tests included in the already available coin package, and provides an independent validation of coin. We review analysis methods for interval-censored data, and we describe and show how to use the interval and perm packages.

Suggested Citation

  • Fay, Michael P. & Shaw, Pamela A., 2010. "Exact and Asymptotic Weighted Logrank Tests for Interval Censored Data: The interval R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i02).
    • Handle: RePEc:jss:jstsof:v:036:i02
    DOI: http://hdl.handle.net/10.18637/jss.v036.i02
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    Cited by:

    1. Reynkens, Tom & Verbelen, Roel & Beirlant, Jan & Antonio, Katrien, 2017. "Modelling censored losses using splicing: A global fit strategy with mixed Erlang and extreme value distributions," Insurance: Mathematics and Economics, Elsevier, vol. 77(C), pages 65-77.
    2. Touraine, Célia & Gerds, Thomas A. & Joly, Pierre, 2017. "SmoothHazard: An R Package for Fitting Regression Models to Interval-Censored Observations of Illness-Death Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i07).
    3. Audrey Boruvka & Richard J. Cook, 2015. "A Cox-Aalen Model for Interval-censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 414-426, June.
    4. Pan, Chun & Cai, Bo & Wang, Lianming & Lin, Xiaoyan, 2014. "Bayesian semiparametric model for spatially correlated interval-censored survival data," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 198-208.
    5. Martin Søndergaard Jørgensen & Rodrigo Labouriau & Birgit Olesen, 2019. "Seed size and burial depth influence Zostera marina L. (eelgrass) seed survival, seedling emergence and initial seedling biomass development," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-16, April.
    6. Adam Čabla & Ivana Malá, 2017. "Využití metod analýzy přežití pro modelování doby nezaměstnanosti v České republice [The Use of Survival Analysis Methods for the Modelling of Unemployment in the Czech Republic]," Politická ekonomie, Prague University of Economics and Business, vol. 2017(4), pages 501-519.
    7. Chen, Yuhui & Hanson, Timothy E., 2014. "Bayesian nonparametric k-sample tests for censored and uncensored data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 335-346.
    8. Huang, Luling & Nock, Destenie, 2024. "Estimating the income-related inequality aversion to energy limiting behavior in the United States," Energy Economics, Elsevier, vol. 136(C).
    9. Adam Čabla & Ivana Malá, 2017. "Modelling of Unemployment Duration in the Czech Republic," Prague Economic Papers, Prague University of Economics and Business, vol. 2017(4), pages 438-449.
    10. Oyarzun, Carlos & Sanjurjo, Adam & Nguyen, Hien, 2017. "Response functions," European Economic Review, Elsevier, vol. 98(C), pages 1-31.

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