International Biometric Society A comparison of methods for analysing incomplete longitudinal binary data
نویسندگان
چکیده
One popular method for analysing correlated binary data is Generalised Estimating Equations (GEE). It is well-known that the validity of this method in its simplest form when the data are incomplete relies on the often implausible assumption of Missing Completely at Random (MCAR). However, there are conditions under which the MCAR assumption can be relaxed to Missing at Random (MAR). Variants of GEE have been proposed that allow more generally for MAR mechanisms, namely through the introduction of inverse probability weights in methods proposed by James Robins and colleagues. Others favour a combination of multiple imputation and GEE known as MI-GEE.
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