# MIsim: Example of a simulation study on missing data In rsimsum: Analysis of Simulation Studies Including Monte Carlo Error

 MIsim R Documentation

## Example of a simulation study on missing data

### Description

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.

```MIsim

MIsim2
```

### 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

### Examples

```data("MIsim", package = "rsimsum")
data("MIsim2", package = "rsimsum")
```

rsimsum documentation built on March 22, 2022, 5:08 p.m.