1. Methods Inverse probability weighting –Can you predict probability of response? –Difficulties if more than one missing outcome or covariate Joint model of outcome and inclusion in sample (Heckman) –Need to find independent “instruments” (Multiple) imputation – chained equations or full data (e.g. EM) –Adds to complexity of model –Difficulties for categorical variables

2. Recommendations Magnitude of bias introduced by missing data unknown Importance of sensitivity analysis Need for guidelines on reporting of missing data analyses –Report complete cases and missing data methods –Complete case necessary even with informative dropout (e.g. death, cognitive function)

3. Wish list Creation of complete multiple imputed data sets –Different studies use different imputation models therefore different data sets Need for training