A dataset from a simulation study comparing different ways to handle missing covariates when fitting a Cox model (White and Royston, 2009).
One thousand datasets were simulated, each containing normally distributed covariates \(x\) and \(z\) and time-to-event outcome.
Both covariates have 20\
Each simulated dataset was analysed in three ways.
A Cox model was fit to the complete cases (CC).
Then two methods of multiple imputation using chained equations (van Buuren, Boshuizen, and Knook, 1999) were used.
The MI_LOGT method multiply imputes the missing values of \(x\) and \(z\) with the outcome included as \(\log (t)\) and \(d\), where \(t\) is the survival time and \(d\) is the event indicator.
The MI_T method is the same except that \(\log (t)\) is replaced by \(t\) in the imputation model.
The results are stored in long format.