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Adjusting for Multiple Treatment Switching - Stratified Two-stage Method using G-estimation

Abstract Number:

2547 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Luoying Yang (1), Debarghya Nandi (2)

Institutions:

(1) Bristol Myers Squibb, N/A, (2) University of Illinois At Chicago, N/A

Co-Author:

Debarghya Nandi  
University of Illinois At Chicago

First Author:

Luoying Yang  
Bristol Myers Squibb

Presenting Author:

Luoying Yang  
N/A

Abstract Text:

In oncology clinical trials, estimates of OS is often confounded because some patients in the control group switch to the experimental treatment. Several switching adjustment methods have been developed to adjust for treatment switching, such as RPSFTM and two-stage method. However, many clinical trials now have patients in the control group switching to different treatment regimens other than the experimental treatments, and most of the existing methods still assume all switchers switch to the same treatment thus cannot handle multilevel switching. Stratified RPSFTM has been proposed to adjust for multilevel switching, however, the method does not change RPSFTM's assumption that treatment effect is the same for all participants regardless of when treatment is received. Two-stage method adjusts for switching that occurs after a specific disease-related time-point, which is a more practical assumption than RPSFTM. Thus, we extended the two-stage method to stratified two-stage model to adjust for multilevel treatment switching and outperforms other methods when the treatment effect is strong and when there is ineligible difference between the switching treatments.

Keywords:

Treatment switching|Two-stage method|Multi-level switching|Randomized Clinical Trial|RPSFTM|

Sponsors:

Biopharmaceutical Section

Tracks:

Clinical Trial Design

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