|
Activity Number:
|
135
- Multiplicity, Missing Data and Other Topics
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 9, 2021 : 1:30 PM to 3:20 PM
|
|
Sponsor:
|
Biopharmaceutical Section
|
|
Abstract #318236
|
|
|
Title:
|
A Novel Return-to-Baseline Imputation Method for Missing Data in Clinical Trials
|
|
Author(s):
|
Biyue Dai* and Yongming Qu
|
|
Companies:
|
Eli Lilly and Company and Eli Lilly and Company
|
|
Keywords:
|
missing at random;
return-to-baseline imputation
|
|
Abstract:
|
Return-to-baseline is an important method to impute missing values resulting from using certain hypothetical strategies to handle intercurrent events in clinical trials. It can also serve as a sensitivity analysis for handling missing values. Current return-to-baseline approaches seen in literature and in practice inflate the variability of the “complete" dataset after imputation and lead to biased mean estimators under the assumption of missing at random (MAR). In this presentation, we first provide a set of criteria that a return-to-baseline imputation method should satisfy. Under this framework, we propose a novel return-to-baseline imputation method. Simulations show the completed data based on the new method have the proper distribution, and the estimators based on the new imputation method outperform the traditional method in terms of both bias and variance, under the MAR assumption. The new method can be implemented easily with the existing multiple imputation procedures in commonly used statistical packages.
|
Authors who are presenting talks have a * after their name.
