Missing Data Imputation in Clinical Trials Using Utility-Based Regression and Sampling Approach

• Activity Number: 231 - SPEED: SPAAC SESSION I
• Type: Topic-Contributed
• Date/Time: Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Halimu N. Haliduola* and Frank Bretz and Ulrich Mansmann
• Companies: Institute for Medical Information Processing, Biometry and Epidemiology – IBE, LMU Munich and Novartis AG and Institute for Medical Information Processing, Biometry and Epidemiology – IBE, LMU Munich
• Keywords: Clinical Trial; Missing Data; Utility-Based Regression; SMOTER

Abstract

Standard predictive error measures of regression (e.g., mean squared error) are not suitable for imbalanced learning problems, such as in clinical trials where extreme values tend to be missing not at random (MNAR). We investigate hybrid imbalanced learning approaches that combine utility-based regression (UBR) with synthetic minority oversampling technique for regression (SMOTER) in cross-sectional trial settings. UBR optimizes the product of the conditional probability density (estimated by quantile regression forests) and a utility surface which takes the relevance of the target variable value and the prediction error into account. SMOTER oversamples the relevant rare cases. Simulations show that the proposed method provides plausible predictions and reduces the bias for realistic missing data scenarios (i.e., mixture of MCAR, MAR, and MNAR data) when compared with standard approaches like random forests and multiple imputation. The extensions of the proposed method to longitudinal trial settings are of interest.