Patrick Royston () (MRC Clinical Trials Unit, London)
Abstract
Following the seminal publications of Rubin starting about 30 years ago, statisticians have become increasingly aware of the inadequacy of `complete case' analysis of datasets with missing observations. In medicine, for example, observations may be missing in a sporadic way for different covariates; and a complete-case analysis may omit as many as half of the available cases. `Hotdeck' imputation was implemented in Stata by Mander and Clayton (1999). However, this technique may perform poorly in the common case when many rows of data have at least one missing value. In this talk, I will describe an implementation for Stata of the `MICE' method of multiple multivariate imputation described by van Buuren et al. (1999) (see also www.multiple-imputation.com). MICE stands for Multivariate Imputation by Chained Equations. The basic idea of data analysis with multiple imputation is to create a small number (e.g. 3-5) copies of the data, each of which has the missing values suitably imputed. Then, each complete dataset is analysed independently. Estimates of parameters of interest are averaged across the copies to give a single estimate. Standard errors are computed according to the `Rubin rules' (Rubin 1987), devised to allow for the between- and within-imputation components of variation in the parameter estimates. In the talk, I will present briefly five ado-files. mvis creates multiple multivariate imputations. uvis imputes missing values for a single variable as a function of several covariates, each with complete data. micombine fits a wide variety of regression models to a multiply imputed dataset, combining the estimates using Rubin's rules. micombine supports survival analysis models (stcox and streg), categorical data models, generalised linear models, and more. Finally, misplit and mijoin are utilities to inter-convert datasets created by mvis and by Carlin et al. (2003)'s miset routine. The use of the routines will be illustrated by example.
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