Journal article

Evaluation of a weighting approach for performing sensitivity analysis after multiple imputation

Panteha Hayati Rezvan, Ian R White, Katherine J Lee, John B Carlin, Julie A Simpson



BACKGROUND: Multiple imputation (MI) is a well-recognised statistical technique for handling missing data. As usually implemented in standard statistical software, MI assumes that data are 'Missing at random' (MAR); an assumption that in many settings is implausible. It is not possible to distinguish whether data are MAR or 'Missing not at random' (MNAR) using the observed data, so it is desirable to discover the impact of departures from the MAR assumption on the MI results by conducting sensitivity analyses. A weighting approach based on a selection model has been proposed for performing MNAR analyses to assess the robustness of results obtained under standard MI to departures from MAR. ME..

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Awarded by National Health and Medical Research Council

Awarded by Medical Research Council

Funding Acknowledgements

This work was supported by funding from the National Health and Medical Research Council: a Centre of Research Excellence grant, ID 1035261, awarded to the Victorian Centre of Biostatistics (ViCBiostat), and Career Development Fellowship ID 1053609(KJL). PHR is funded by an Australian Postgraduate Award. IRW was supported by the Medical Research Council [Unit Programme number U105260558].