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Random‐effects meta‐analyses are very commonly used in medical statistics. Recent methodological developments include multivariate (multiple outcomes) and network (multiple treatments) meta‐analysis. Here, we provide a new model and corresponding estimation procedure for multivariate network meta‐analysis, so that multiple outcomes and treatments can be included in a single analysis. Our new multivariate...
It is not uncommon in follow‐up studies to make multiple attempts to collect a measurement after baseline. Recording whether these attempts are successful or not provides useful information for the purposes of assessing the missing at random (MAR) assumption and facilitating missing not at random (MNAR) modeling. This is because measurements from subjects who provide this data after multiple failed...
Summary Two approaches commonly used to deal with missing data are multiple imputation (MI) and inverse‐probability weighting (IPW). IPW is also used to adjust for unequal sampling fractions. MI is generally more efficient than IPW but more complex. Whereas IPW requires only a model for the probability that an individual has complete data (a univariate outcome), MI needs a model for the joint distribution...
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