General and Feasible Tests with Multiply-Imputed Datasets

TL;DR

This paper introduces a unified, practical multiple imputation testing procedure called stacked multiple imputation (SMI) that handles various tests without restrictive assumptions or infinite imputations.

Contribution

The paper presents SMI, a general MI testing framework that unifies Wald's, likelihood ratio, and Rao's score tests without requiring EOMI or infinite imputations.

Findings

SMI performs multiple tests with a single algorithm.

SMI does not require EOMI or infinite imputations.

SMI simplifies implementation for analysts.

Abstract

Multiple imputation (MI) is a technique especially designed for handling missing data in public-use datasets. It allows analysts to perform incomplete-data inference straightforwardly by using several already imputed datasets released by the dataset owners. However, the existing MI tests require either a restrictive assumption on the missing-data mechanism, known as equal odds of missing information (EOMI), or an infinite number of imputations. Some of them also require analysts to have access to restrictive or non-standard computer subroutines. Besides, the existing MI testing procedures cover only Wald's tests and likelihood ratio tests but not Rao's score tests, therefore, these MI testing procedures are not general enough. In addition, the MI Wald's tests and MI likelihood ratio tests are not procedurally identical, so analysts need to resort to distinct algorithms for implementation. In this paper, we propose a general MI procedure, called stacked multiple imputation (SMI), for performing Wald's tests, likelihood ratio tests and Rao's score tests by a unified algorithm. SMI requires neither EOMI nor an infinite number of imputations. It is particularly feasible for analysts as they just need to use a complete-data testing device for performing the corresponding incomplete-data test.