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% of their values deleted independently of all other variables so the data became missing completely at random (Little and Rubin, 2002).
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.

## Format

A data frame with 3,000 rows and 4 variables:

`dataset`

Simulated dataset number.`method`

Method used (`CC`

,`MI_LOGT`

or`MI_T`

).`b`

Point estimate.`se`

Standard error of the point estimate.

An object of class `tbl_df`

(inherits from `tbl`

, `data.frame`

) with 3000 rows and 5 columns.

## Note

`MIsim2`

is a version of the same dataset with the `method`

column split into two columns, `m1`

and `m2`

.

## References

White, I.R., and P. Royston. 2009. Imputing missing covariate values for the Cox model. Statistics in Medicine 28(15):1982-1998 doi:10.1002/sim.3618