Abstract Number:
3697
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Naimin Jing (1), Fang Liu (2), Devan Mehrotra (3)
Institutions:
(1) Merck & Co., N/A, (2) Merck, N/A, (3) Merck & Co., Inc., N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
We focus on two-arm (active, control) randomized clinical trials where the primary estimand is defined using the treatment policy strategy to handle intercurrent events. In such settings, valid statistical estimation and inference is easy with complete follow-up for all subjects regardless of their adherence to assigned treatment. However, it is often the case that some subjects drop out from the study before their endpoint can be assessed, resulting in missing data. This problem is often tackled using mixed‐effects model repeated measures (MMRM) analyses. An alternative is using control-based imputation (CBI) methods that impute missing data in the active arm using data from the control arm. The imputation can be done either separately for each active arm dropout (e.g., jump-to-reference; J2R) or at a mean level for the pool of active arm dropouts (control-based mean imputation; CBMI). We use simulations to compare the performance of MMRM, J2R, CBMI and other approaches (including using a common worse-rank score for all dropouts) when data are either missing not at random (MNAR) in both arms, or MNAR in the active arm but missing completely at random (MCAR) in the control arm.
Keywords:
dropout|missing not at random|control-based imputation| | |
Sponsors:
Biopharmaceutical Section
Tracks:
Missing Data
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