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A Bayesian approach for sensitivity analysis of incomplete multivariate longitudinal data with potential nonrandom dropout

Listed author(s):
  • Samaneh Mahabadi

    ()

  • Mojtaba Ganjali

    ()

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    Experiments involving repeated observations of multivariate outcomes are common in biomedical and public health researches which lead to multivariate longitudinal data. These kinds of data have a unique property in the sense that they allow the researcher to study the joint evolution of the multiple outcomes over the time. Recently, there has been a considerable amount of interest on using Bayesian modelling of longitudinal data, data which commonly suffer from incomplete observations. Those Bayesian models for longitudinal data that rely on the ignorability assumption of the dropout mechanism might give misleading inferences. Hence, there is a need to further study the impact of departures from the ignorability assumption on the Bayesian estimates of the model parameters. Current methodology for Bayesian sensitivity analysis mostly involves single response variable in both cross-sectional and longitudinal studies. In this paper, we propose a multivariate extension of the Bayesian index of sensitivity to non-ignorability for the general case of multivariate longitudinal studies with the possibility of having mixed correlated outcomes and a vector of multiple non-ignorability parameters in the missing mechanism. To simultaneously model the mixed responses over the time, we use a random effect latent variable approach. We illustrate the method conducting some simulation studies and analyzing a real data set from a longitudinal study for the comparison of two oral treatments for toenail dermatophyte onychomycosis. Copyright Sapienza Università di Roma 2015

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    File URL: http://hdl.handle.net/10.1007/s40300-015-0063-6
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    Article provided by Springer & Sapienza Università di Roma in its journal METRON.

    Volume (Year): 73 (2015)
    Issue (Month): 3 (December)
    Pages: 397-417

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    Handle: RePEc:spr:metron:v:73:y:2015:i:3:p:397-417
    DOI: 10.1007/s40300-015-0063-6
    Contact details of provider: Web page: http://www.springer.com

    Web page: http://www.uniroma1.it/

    Order Information: Web: http://www.springer.com/economics/journal/40300

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    1. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    2. James Robins & Andrea Rotnitzky & Stijn Vansteelandt, 2007. "Discussions," Biometrics, The International Biometric Society, vol. 63(3), pages 650-653, 09.
    3. John Copas & Shinto Eguchi, 2001. "Local sensitivity approximations for selectivity bias," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 871-895.
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