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Effects of covariates on alternating recurrent events in accelerated failure time models

Author

Listed:
  • Chatterjee Moumita

    (Department of Mathematics and Statistics, Aliah University, IIA/27, New Town, Kolkata 700160, India)

  • Sen Roy Sugata

    (Department of Statistics, University of Calcutta, 35, Ballygunge Circular Road, Calcutta 700019, India)

Abstract
In this article, we model alternately occurring recurrent events and study the effects of covariates on each of the survival times. This is done through the accelerated failure time models, where we use lagged event times to capture the dependence over both the cycles and the two events. However, since the errors of the two regression models are likely to be correlated, we assume a bivariate error distribution. Since most event time distributions do not readily extend to bivariate forms, we take recourse to copula functions to build up the bivariate distributions from the marginals. The model parameters are then estimated using the maximum likelihood method and the properties of the estimators studied. A data on respiratory disease is used to illustrate the technique. A simulation study is also conducted to check for consistency.

Suggested Citation

  • Chatterjee Moumita & Sen Roy Sugata, 2021. "Effects of covariates on alternating recurrent events in accelerated failure time models," The International Journal of Biostatistics, De Gruyter, vol. 17(2), pages 295-315, November.
    • Handle: RePEc:bpj:ijbist:v:17:y:2021:i:2:p:295-315:n:6
    DOI: 10.1515/ijb-2019-0099
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