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Regression analysis of sparse asynchronous longitudinal data

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  • Hongyuan Cao
  • Donglin Zeng
  • Jason P. Fine

Abstract

type="main" xml:id="rssb12086-abs-0001"> We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus.

Suggested Citation

  • Hongyuan Cao & Donglin Zeng & Jason P. Fine, 2015. "Regression analysis of sparse asynchronous longitudinal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(4), pages 755-776, September.
    • Handle: RePEc:bla:jorssb:v:77:y:2015:i:4:p:755-776
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    File URL: http://hdl.handle.net/10.1111/rssb.2015.77.issue-4
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    Cited by:

    1. Sun, Dayu & Zhao, Hui & Sun, Jianguo, 2021. "Regression analysis of asynchronous longitudinal data with informative observation processes," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    2. Zhuowei Sun & Hongyuan Cao & Li Chen, 2022. "Regression analysis of additive hazards model with sparse longitudinal covariates," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 263-281, April.
    3. Zhong, Rou & Liu, Shishi & Li, Haocheng & Zhang, Jingxiao, 2022. "Robust functional principal component analysis for non-Gaussian longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    4. Xinyue Chang & Yehua Li & Yi Li, 2023. "Asynchronous and error‐prone longitudinal data analysis via functional calibration," Biometrics, The International Biometric Society, vol. 79(4), pages 3374-3387, December.
    5. Ting Li & Huichen Zhu & Tengfei Li & Hongtu Zhu, 2023. "Asynchronous functional linear regression models for longitudinal data in reproducing kernel Hilbert space," Biometrics, The International Biometric Society, vol. 79(3), pages 1880-1895, September.

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