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Bootstrap likelihood ratio confidence bands for survival functions under random censorship and its semiparametric extension

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  • Subramanian, Sundarraman

Abstract

Simultaneous confidence bands for survival functions, from randomly right censored data, can be computed by inverting likelihood ratio functions based on appropriate thresholds. Sometimes, however, the requisite asymptotic distributions are intractable, or thresholds based on Brownian bridge approximations are not easy to obtain when simultaneous confidence bands over only sub-regions are possible or desired. We obtain the thresholds by bootstrapping (i) a nonparametric likelihood ratio function via censored data bootstrap and (ii) a semiparametric adjusted likelihood ratio function via a two-stage bootstrap that utilizes a model for the second stage. These two scenarios are grounded respectively in standard random censorship and its semiparametric extension introduced by Dikta. The two bootstraps, which are different in the way resampling is done, are shown to have asymptotic validity. The respective confidence bands are neighborhoods of the well-known Kaplan–Meier estimator and the more recently developed Dikta’s semiparametric counterpart. As evidenced by a validation study, both types of confidence bands provide approximately correct coverage. The model-based confidence bands, however, are tighter than the nonparametric ones. Two sensitivity studies reveal that the model-based method performs well when standard binary regression models are fitted, indicating its robustness to misspecification as well as its practical applicability. An illustration is given using real data.

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  • Subramanian, Sundarraman, 2016. "Bootstrap likelihood ratio confidence bands for survival functions under random censorship and its semiparametric extension," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 58-81.
    • Handle: RePEc:eee:jmvana:v:147:y:2016:i:c:p:58-81
    DOI: 10.1016/j.jmva.2016.01.002
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    1. W. Stute, 1992. "Strong consistency of the MLE under random censoring," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 39(1), pages 257-267, December.
    2. Einmahl, J.H.J. & McKeague, I.W., 1999. "Confidence tubes for multiple quantile plots via empirical likelihood," Other publications TiSEM b64493f8-1c01-40fd-b16d-7, Tilburg University, School of Economics and Management.
    3. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258, October.
    4. Anastasios A. Tsiatis, 2002. "Multiple imputation methods for testing treatment differences in survival distributions with missing cause of failure," Biometrika, Biometrika Trust, vol. 89(1), pages 238-244, March.
    5. Ming Yuan, 2005. "Semiparametric censorship model with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(2), pages 489-514, December.
    6. Subramanian, Sundarraman, 2012. "Model-based likelihood ratio confidence intervals for survival functions," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 626-635.
    7. Mondal, Shoubhik & Subramanian, Sundarraman, 2014. "Model assisted Cox regression," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 281-303.
    8. Koenker, Roger & Yoon, Jungmo, 2009. "Parametric links for binary choice models: A Fisherian-Bayesian colloquy," Journal of Econometrics, Elsevier, vol. 152(2), pages 120-130, October.
    9. Li, Gang, 1995. "On nonparametric likelihood ratio estimation of survival probabilities for censored data," Statistics & Probability Letters, Elsevier, vol. 25(2), pages 95-104, November.
    10. Shoubhik Mondal & Sundarraman Subramanian, 2016. "Simultaneous confidence bands for Cox regression from semiparametric random censorship," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 122-144, January.
    11. Dikta, Gerhard & Kvesic, Marsel & Schmidt, Christian, 2006. "Bootstrap Approximations in Model Checks for Binary Data," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 521-530, June.
    12. Dikta, Gerhard, 2014. "Asymptotically efficient estimation under semi-parametric random censorship models," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 10-24.
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    1. Subramanian, Sundarraman, 2022. "Simultaneous confidence bands for survival functions from twice censorship," Statistics & Probability Letters, Elsevier, vol. 186(C).

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