A multiple regression imputation method with application to sensitivity analysis under intermittent missingness

Abstract

Missing data is a common problem in general applied studies, and specially in clinical trials. For implementing sensitivity analysis, several multiple imputation methods exist, like sequential imputation, which restricts to monotone missingness, and Bayesian, where the imputation and analysis models differ, entailing overestimation of variance. Also, full conditional specification provides a conditional interpretation of sensitivity parameters, requiring further calibration to get the desired marginal interpretation. We propose in this paper a multiple imputation procedure, based on a multivariate linear regression model, which keeps compatibility in sensitivity analysis under intermittent missingness, providing a marginal interpretation of the elicited parameters. Simulation studies show that the method behaves well with longitudinal data and remains robust under demanding constraints. We conclude the possibility of situations not covered by the existing methods and well suited for our proposal, which allows more efficient handling of a given multivariate linear regression structure. Its use is illustrated in a real case study, where a sensitivity analysis is accomplished.

Keywords:

• Missing data
• multiple imputation
• sensitivity analysis
• clinical trial
• Gibbs sampler

Additional information

Funding

Financial support from the VLIR-UOS JOINT-project “A Cuban-Flemish Training and Research Program in Data Science and Big Data Analysis” (2018–2020) is gratefully acknowledged.