Handling Nonmonotone Missing Data with Available Complete-Case Missing Value Assumption

TL;DR

This paper introduces the ACCMV assumption for nonmonotone missing data, enabling nonparametric identification and robust estimation methods even with limited complete data, demonstrated through simulations and real health data.

Contribution

The paper proposes the ACCMV assumption for handling nonmonotone MNAR data, along with new estimators and theoretical validation for practical data analysis.

Findings

ACCMV assumption enables identification with few complete observations

Proposed estimators are asymptotically valid and efficient

Method successfully applied to electronic health record data

Abstract

Nonmonotone missing data is a common problem in scientific studies. The conventional ignorability and missing-at-random (MAR) conditions are unlikely to hold for nonmonotone missing data and data analysis can be very challenging with few complete data. In this paper, we introduce the available complete-case missing value (ACCMV) assumption for handling nonmonotone and missing-not-at-random (MNAR) problems. Our ACCMV assumption is applicable to data set with a small set of complete observations and we show that the ACCMV assumption leads to nonparametric identification of the distribution for the variables of interest. We further propose an inverse probability weighting estimator, a regression adjustment estimator, and a multiply-robust estimator for estimating a parameter of interest. We studied the underlying asymptotic and efficiency theories of the proposed estimators. We show the validity of our method with simulation studies and further illustrate the applicability of our method by applying it to a diabetes data set from electronic health records.