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

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.

缺失数据是各类应用研究中的常见问题，在临床试验中尤为显著。为开展敏感性分析（sensitivity analysis），现有多重插补（multiple imputation）方法包括两类典型方案：其一为仅适用于单调缺失场景的序列插补法，其二为插补模型与分析模型存在差异、易导致方差高估的贝叶斯插补法。此外，全条件设定（full conditional specification）可对敏感性参数给出条件层面的解释，但需经过额外校准才能实现预期的边际解释。本文提出一种基于多元线性回归模型的多重插补流程，该方法在间歇性缺失场景下可保障敏感性分析的兼容性，并对所确定的参数给出边际层面的解释。模拟研究结果表明，所提方法在纵向数据（longitudinal data）场景下表现优异，且在严苛约束条件下仍具备稳健性。本文进一步论证了现有方法未能覆盖的一类适用场景，而该场景恰好适配本文所提方案，可实现对给定多元线性回归结构的更高效处理。最后通过一则实际案例研究展示了该方法的应用流程，并完成了相应的敏感性分析。