Incomplete data is a common complication in applied research. In this study, we use simulation to compare two approaches to the multiple imputation of a continuous predictor: multiple imputation through chained equations and multivariate normal imputation. This study extends earlier work by...

Standard statistical analyses of observational data often exclude valuable information from individuals with incomplete measurements. This may lead to biased estimates of the treatment effect and loss of precision. The issue of missing data for inverse probability of treatment weighted...

We present an update of mim, a program for managing multiply im- puted datasets and performing inference (estimating parameters) using Rubin’s rules for combining estimates from imputed datasets. The new features of particular importance are an option for estimating the Monte Carlo error (due...

A controlled clinical trial was conducted to investigate the efficacy effect of a chemical compound in the treatment of Premenstrual Dysphoric Disorder (PMDD). The data from the trial showed a non-monotone pattern of missing data and an ante-dependence covariance structure. A new analytical...

Following the seminal publications of Rubin about thirty 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,...

The method of multiple imputation (MI) is used increasingly for analyzing datasets with missing observations. Two sets of tasks are required in order to implement the method: (a) generating multiple complete datasets in which missing values have been imputed by simulating from an appropriate...

Widely used methods for analyzing missing data can be biased in small samples. To understand these biases, we evaluate in detail the situation where a small univariate normal sample, with values missing at random, is analyzed using either observed-data maximum likelihood (ML) or multiple...