Missing outcome data are commonly encountered in clinical trials and may compromise the validity of the estimated treatment effects. Meta-analysts typically assume that the missing data problem has been solved at the meta-analysis level and conduct an available case analysis. However, bias from missing outcome data is accumulated in a synthesis of those trials using meta-analysis. In this webinar, we present methods for estimating meta-analytic summary treatment effects when some outcome data are missing by using a statistical (pattern mixture) model to allow for uncertainty in the missing information. We quantify the degree of departure from the MAR assumption by introducing an informative parameter that relate the outcome in the missing data to that in the observed. This parameter is not informed by the data and we resort to expert opinion to inform or conduct a sensitivity analysis. We present a Stata command that applies the suggested methodology i) for both dichotomous and continuous outcomes ii) for pairwise and network meta-analysis. We illustrate the method using examples from mental health trials where outcome data are rarely missing at random (MAR).
This webinar is organised by the Cochrane Statistical Methods Group, with the support from Cochrane Membership, Learning and Support Services. The webinar will consist of a presentation, followed by a question and answers session.
Dimitris Mavridis, Department of Primary Education, University of Ioannina, Ioannina, Greece.
Anna Chaimani, Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS-UMR1153), Inserm / Université Paris Descartes
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