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MCMC-based estimation methods for continuous longitudinal data with non-random (non)-monotone missingness

Author

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  • Sotto, Cristina
  • Beunckens, Caroline
  • Molenberghs, Geert
  • Kenward, Michael G.

Abstract

The analysis of incomplete longitudinal data requires joint modeling of the longitudinal outcomes (observed and unobserved) and the response indicators. When non-response does not depend on the unobserved outcomes, within a likelihood framework, the missingness is said to be ignorable, obviating the need to formally model the process that drives it. For the non-ignorable or non-random case, estimation is less straightforward, because one must work with the observed data likelihood, which involves integration over the missing values, thereby giving rise to computational complexity, especially for high-dimensional missingness. The stochastic EM algorithm is a variation of the expectation-maximization (EM) algorithm and is particularly useful in cases where the E (expectation) step is intractable. Under the stochastic EM algorithm, the E-step is replaced by an S-step, in which the missing data are simulated from an appropriate conditional distribution. The method is appealing due to its computational simplicity. The SEM algorithm is used to fit non-random models for continuous longitudinal data with monotone or non-monotone missingness, using simulated, as well as case study, data. Resulting SEM estimates are compared with their direct likelihood counterparts wherever possible.

Suggested Citation

  • Sotto, Cristina & Beunckens, Caroline & Molenberghs, Geert & Kenward, Michael G., 2011. "MCMC-based estimation methods for continuous longitudinal data with non-random (non)-monotone missingness," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 301-311, January.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:1:p:301-311
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    References listed on IDEAS

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    1. Jansen, Ivy & Hens, Niel & Molenberghs, Geert & Aerts, Marc & Verbeke, Geert & Kenward, Michael G., 2006. "The nature of sensitivity in monotone missing not at random models," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 830-858, February.
    2. Gad, Ahmed M. & Ahmed, Abeer S., 2006. "Analysis of longitudinal data with intermittent missing values using the stochastic EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2702-2714, June.
    3. P. Diggle & M. G. Kenward, 1994. "Informative Drop‐Out in Longitudinal Data Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 49-73, March.
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    1. Wang, Liqun & Lee, Chel Hee, 2014. "Discretization-based direct random sample generation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1001-1010.
    2. Xie, Hui, 2012. "Analyzing longitudinal clinical trial data with nonignorable missingness and unknown missingness reasons," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1287-1300.

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