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Stat Med. 2018 Mar 15;37(6):996-1008. doi: 10.1002/sim.7563. Epub 2017 Nov 23.

Semiparametric regression analysis for alternating recurrent event data.

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Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94158, USA.
Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA.
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA.
Biostatistics Core, Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA.


Alternating recurrent event data arise frequently in clinical and epidemiologic studies, where 2 types of events such as hospital admission and discharge occur alternately over time. The 2 alternating states defined by these recurrent events could each carry important and distinct information about a patient's underlying health condition and/or the quality of care. In this paper, we propose a semiparametric method for evaluating covariate effects on the 2 alternating states jointly. The proposed methodology accounts for the dependence among the alternating states as well as the heterogeneity across patients via a frailty with unspecified distribution. Moreover, the estimation procedure, which is based on smooth estimating equations, not only properly addresses challenges such as induced dependent censoring and intercept sampling bias commonly confronted in serial event gap time data but also is more computationally tractable than the existing rank-based methods. The proposed methods are evaluated by simulation studies and illustrated by analyzing psychiatric contacts from the South Verona Psychiatric Case Register.


accelerated failure time model; alternating renewal process; gap times; recurrent events

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