Missing data: Our view of the state of the art.

TLDR

Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing‑data problem, review methods, offer advice, and highlight unresolved issues. They recommend two main approaches—maximum likelihood and Bayesian multiple imputation—and discuss newer methods for non‑MAR data. The review clarifies misconceptions about MAR, discredits older procedures, and suggests that emerging techniques may extend the current state of the art.

Abstract

Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.