# 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.

### Usage

``````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 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.3618")}

### Examples

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

rsimsum documentation built on May 29, 2024, 2:18 a.m.