A novel analytical solution for reference based and delta adjustment imputation: faster and more efficient than multiple imputation

A. Garcia Hernandez, T. Perez Perez, D. Rizopoulos, M. C. Pardo Llorente

In clinical trials, when the response is not observed after treatment discontinuation, we face a missing not at random problem that can be addressed using reference-based imputation or delta adjustment. The most popular methodology uses multiple imputation.

We propose a new formulation to tackle this problem fully analytically. We model the process of interest (Z) using its association with a) time to drug discontinuation T and b) the longitudinal response in a hypothetical world without drug discontinuation Y. We use the equation Z(t) = Y(t) + Δ(t)·I(t > T) where Δ(t) is used to quantify the unobserved difference between Y(t) and Z(t) after T. We translate six popular imputation rules into a Δ(t) and build close-equations estimators for the marginal means of Z.

Our model allows implementing a broad range of rules. It is faster and more efficient than multiple imputation and avoids standard error over-estimation − known issue for the solution based on multiple imputation.

Keywords: Missing not at random, reference-based imputation, delta adjustment.

Scheduled

Biostatistics I
June 9, 2022  10:10 AM
A13

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