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Multiplicative rates model for recurrent events in case-cohort studies

Author

Listed:
  • Poulami Maitra

    (University of North Carolina at Chapel Hill)

  • Leila D. A. F. Amorim

    (Federal University of Bahia)

  • Jianwen Cai

    (University of North Carolina at Chapel Hill)

Abstract

In large prospective cohort studies, accumulation of covariate information and follow-up data make up the majority of the cost involved in the study. This might lead to the study being infeasible when there are some expensive variables and/or the event is rare. Prentice (Biometrika 73(1):1–11, 1986) proposed the case-cohort study for time to event data to tackle this problem. There has been extensive research on the analysis of univariate and clustered failure time data, where the clusters are formed among different individuals under case-cohort sampling scheme. However, recurrent event data are quite common in biomedical and public health research. In this paper, we propose case-cohort sampling schemes for recurrent events. We consider a multiplicative rates model for the recurrent events and propose a weighted estimating equations approach for parameter estimation. We show that the estimators are consistent and asymptotically normally distributed. The proposed estimator performed well in finite samples in our simulation studies. For illustration purposes, we examined the association between prior occurrence of measles on acute lower respiratory tract infections (ALRI) among young children in Brazil.

Suggested Citation

  • Poulami Maitra & Leila D. A. F. Amorim & Jianwen Cai, 2020. "Multiplicative rates model for recurrent events in case-cohort studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(1), pages 134-157, January.
    • Handle: RePEc:spr:lifeda:v:26:y:2020:i:1:d:10.1007_s10985-019-09466-0
    DOI: 10.1007/s10985-019-09466-0
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    References listed on IDEAS

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    1. Shou-En Lu & Joanna H. Shih, 2006. "Case-Cohort Designs and Analysis for Clustered Failure Time Data," Biometrics, The International Biometric Society, vol. 62(4), pages 1138-1148, December.
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    7. S. Kang & J. Cai, 2009. "Marginal hazards model for case-cohort studies with multiple disease outcomes," Biometrika, Biometrika Trust, vol. 96(4), pages 887-901.
    8. Cook, Richard J. & Lawless, Jerald F. & Lakhal-Chaieb, Lajmi & Lee, Ker-Ai, 2009. "Robust Estimation of Mean Functions and Treatment Effects for Recurrent Events Under Event-Dependent Censoring and Termination: Application to Skeletal Complications in Cancer Metastatic to Bone," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 60-75.
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