Comparing multiple imputation methods for systematically missing subject‐level data

Abstract

When conducting research synthesis, the collection of studies that will be combined often do not measure the same set of variables, which creates missing data. When the studies to combine are longitudinal, missing data can occur on the observation-level (time-varying) or the subject-level (non-time-varying). Traditionally, the focus of missing data methods for longitudinal data has been on missing observation-level variables. In this paper, we focus on missing subject-level variables and compare two multiple imputation approaches: a joint modeling approach and a sequential conditional modeling approach. We find the joint modeling approach to be preferable to the sequential conditional approach, except when the covariance structure of the repeated outcome for each individual has homogenous variance and exchangeable correlation. Specifically, the regression coefficient estimates from an analysis incorporating imputed values based on the sequential conditional method are attenuated and less efficient than those from the joint method. Remarkably, the estimates from the sequential conditional method are often less efficient than a complete case analysis, which, in the context of research synthesis, implies that we lose efficiency by combining studies. Copyright © 2015 John Wiley & Sons, Ltd.

Ethnicity and acculturation: Asian American substance use from early adolescence to mature adulthood

“…Missing data is an ubiquitous issue longitudinal survey data (Kline, Andridge and Kaizar 2017), and is present to a modest degree in the current analysis (see Appendix 1 for missingness summary).…”

Individual participant data meta‐analysis of intervention studies with time‐to‐event outcomes: A review of the methodology and an applied example

“…The latter can be achieved by adopting imputation models with mixed effects, which also facilitates imputation of covariates that have not been measured in one or more studies. [138][139][140][141][142] Although the assumptions needed for multiple imputation cannot always be tested or may not always be met, several simulation studies have shown that its use is usually superior to complete-case analysis or the use of missing data indicators. 143 However, caution is still warranted when analyzing imputed data sets from IPD-MA, as in the presence of between-trial heterogeneity these are inherently prone to some degree of incompatibility with the data generation mechanism.…”

A comparison of existing methods for multiple imputation in individual participant data meta-analysis

“…We also focus on this situation. However, we note that several authors have studied imputation methods for missing 'level 1' variables [15][16][17]. In this setting, Kline, Andridge, and Kaizar showed that aggregating 'level 2' variables to construct an FCS conditional regression models for each 'level 1' variable (as proposed by [15]) results in underestimates of the association between the missing variables and the observed level 2 covariates [17].…”