Title: Multiple imputation under intermittent missingness

Abstract

Scope: Missing data is a common problem in general applied studies, and specially in clinical trials. An improper treatment of missing data may have serious implications for the accuracy of inferences of many clinical studies. Then, it is necessary to provide rigorously validated methodological tools that allow tackling this problem. Furthermore, 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. Objectives: The aim of this work is to present 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. Methods used: We conduct two simulation studies, one on a standard setting and another in a more demanding setting, which show that the proposed method of multiple imputation behaves well with longitudinal data and remains robust under demanding constraints. Its use is illustrated in a real case study on Cuban vaccines against COVID-19, where a simulation and a sensitivity analysis are conducted. Conclusion: 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. These situations comprise the delta-adjustment setting, an instance of sensitivity analysis; control-based imputation, an approach to sensitivity analysis that imputes missing data in the test drug group using a model built from the control group; as well as other settings.