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Estimating the hazard functions of two alternating recurrent events in the presence of covariates

Author

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  • Moumita Chatterjee

    (Aliah University)

  • Sugata Sen Roy

    (University of Calcutta)

Abstract

The motivation for this paper is a cystic fibrosis data which records a patient’s times to relapse and times to cure under several recurrences of the disease. The idea is to study the impact of covariates on the hazard rates of two alternately occurring events. The dependence between the times to the two events over the different cycles is modeled through an autoregressive-type setup. The partial likelihood function is then derived and the estimators obtained. The estimators are shown to be consistent and asymptotically normal. The technique is applied to study the motivating data. A simulation study is also conducted to corroborate the results.

Suggested Citation

  • Moumita Chatterjee & Sugata Sen Roy, 2018. "Estimating the hazard functions of two alternating recurrent events in the presence of covariates," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(2), pages 289-304, April.
    • Handle: RePEc:spr:alstar:v:102:y:2018:i:2:d:10.1007_s10182-017-0316-1
    DOI: 10.1007/s10182-017-0316-1
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    References listed on IDEAS

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    1. Chiung-Yu Huang & Mei-Cheng Wang, 2005. "Nonparametric Estimation of the Bivariate Recurrence Time Distribution," Biometrics, The International Biometric Society, vol. 61(2), pages 392-402, June.
    2. Sugata Sen Roy & Moumita Chatterjee, 2015. "Estimating the hazard functions of two alternately occurring recurrent events," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1547-1555, July.
    3. Yan, Jun & Fine, Jason P., 2008. "Analysis of Episodic Data With Application to Recurrent Pulmonary Exacerbations in Cystic Fibrosis Patients," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 498-510, June.
    Full references (including those not matched with items on IDEAS)

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