Print  Close

A Closer Look at Control-Based Imputation for Active Arm Dropouts in Randomized Clinical Trials

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):

Fang Liu  
Merck
Devan Mehrotra  
Merck & Co., Inc.

First Author:

Naimin Jing  
Merck & Co.

Presenting Author:

Naimin Jing  
Merck & Co.

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

Can this be considered for alternate subtype?

Yes

Are you interested in volunteering to serve as a session chair?

No

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

I understand