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 has 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.
MIsimA 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.
White, I.R., and P. Royston. 2009. Imputing missing covariate values for the Cox model. Statistics in Medicine 28(15):1982-1998 10.1002/sim.3618
Little, R.J.A., and D.B. Rubin. 2002. Statistical analysis with missing data. 2nd ed. Hoboken, NJ: Wiley 10.1002/9781119013563
# NOT RUN {
data("MIsim", package = "rsimsum")
# }
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