MiPy: a system for generating multiple imputations

Abstract

Multiple imputation has proven to be a useful mode of inference in the presence of missing data. It is a Monte-Carlo based methodology in which missing values are imputed multiple times by draws from a (typically explicit) imputation model. Generating “proper” imputations under reasonable models for the missing-data and complete-data mechanisms is often a computationally challenging task. The lack of software for generating multiple imputations is a serious impediment to the routine adoption of multiple imputation for handling missing data. Several groups have developed software for generating imputations, most of which is model specific, but none of this software is open, flexible, or extensible. In this paper I will introduce a computer software system, called MiPy, for generating multiple imputations under a wide variety of models using several computational approaches. The system is constructed from a combination of Python, an object-oriented and portable high-level interpreted language, and compiled modules in C, C++, and Fortran. MiPy features a clean syntax, simple GUI, open source, and portability to all of the major operating system platforms. In MiPy, Python can be viewed as the glue language that ties together computationally intensive modules written in lower-level languages.