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Simulation driven inferences for multiply imputed longitudinal datasets

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  • Hakan Demirtas

Abstract

In this article, we demonstrate by simulations that rich imputation models for incomplete longitudinal datasets produce more calibrated estimates in terms of reduced bias and higher coverage rates without duly deflating the efficiency. We argue that the use of supplementary variables that are thought to be potential causes or correlates of missingness or outcomes in the imputation process may lead to better inferential results in comparison to simpler imputation models. The liberal use of these variables is recommended as opposed to the conservative strategy.

Suggested Citation

  • Hakan Demirtas, 2004. "Simulation driven inferences for multiply imputed longitudinal datasets," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(4), pages 466-482, November.
    • Handle: RePEc:bla:stanee:v:58:y:2004:i:4:p:466-482
    DOI: 10.1111/j.1467-9574.2004.00271.x
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    Cited by:

    1. Hakan Demirtas & Donald Hedeker, 2011. "Generating multivariate continuous data via the notion of nearest neighbors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(1), pages 47-55.
    2. Leon, Andrew C. & Hedeker, Donald, 2007. "Quintile stratification based on a misspecified propensity score in longitudinal treatment effectiveness analyses of ordinal doses," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6114-6122, August.
    3. Hakan Demirtas & Robab Ahmadian & Sema Atis & Fatma Ezgi Can & Ilker Ercan, 2016. "A nonnormal look at polychoric correlations: modeling the change in correlations before and after discretization," Computational Statistics, Springer, vol. 31(4), pages 1385-1401, December.
    4. Demirtas, Hakan, 2008. "On imputing continuous data when the eventual interest pertains to ordinalized outcomes via threshold concept," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2261-2271, January.
    5. Yucel, Recai M. & Demirtas, Hakan, 2010. "Impact of non-normal random effects on inference by multiple imputation: A simulation assessment," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 790-801, March.
    6. Sarah Mustillo, 2012. "The Effects of Auxiliary Variables on Coefficient Bias and Efficiency in Multiple Imputation," Sociological Methods & Research, , vol. 41(2), pages 335-361, May.

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