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Tackling non-ignorable dropout in the presence of time varying confounding

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  • Marco Doretti
  • Sara Geneletti
  • Elena Stanghellini

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  • Marco Doretti & Sara Geneletti & Elena Stanghellini, 2016. "Tackling non-ignorable dropout in the presence of time varying confounding," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(5), pages 775-795, November.
    • Handle: RePEc:bla:jorssc:v:65:y:2016:i:5:p:775-795
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    References listed on IDEAS

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    1. Patrick Puhani, 2000. "The Heckman Correction for Sample Selection and Its Critique," Journal of Economic Surveys, Wiley Blackwell, vol. 14(1), pages 53-68, February.
    2. Rhian M. Daniel & Bianca L. De Stavola & Simon N. Cousens, 2011. "gformula: Estimating causal effects in the presence of time-varying confounding or mediation using the g-computation formula," Stata Journal, StataCorp LP, vol. 11(4), pages 479-517, December.
    3. Elja Arjas & Jan Parner, 2004. "Causal Reasoning from Longitudinal Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(2), pages 171-187, June.
    4. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    5. Elizabeth Washbrook & Paul S. Clarke & Fiona Steele, 2014. "Investigating non-ignorable dropout in panel studies of residential mobility," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 239-266, February.
    6. Arjas Elja & Saarela Olli, 2010. "Optimal Dynamic Regimes: Presenting a Case for Predictive Inference," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-21, March.
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