Implementation of statistical features of a Bayesian two-armed RAR trial

Abstract Number:

1921 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Elena Shergina (1), Kimber Richter (2), Chuanwu Zhang (3), Laura Mussulman (4), Niaman Nazir (2), Byron Gajewski (1)

Institutions:

(1) University of Kansas Medical Center, N/A, (2) University of Kansas Medical Centre, Kansas City, KS, (3) Sanofi, N/A, (4) University of Kansas Medical Centre, Kansas City, United States

Co-Author(s):

Kimber Richter  
University of Kansas Medical Centre
Chuanwu Zhang  
Sanofi
Laura Mussulman  
University of Kansas Medical Centre
Niaman Nazir  
University of Kansas Medical Centre
Byron Gajewski  
University of Kansas Medical Center

First Author:

Elena Shergina  
University of Kansas Medical Center

Presenting Author:

Elena Shergina  
University of Kansas Medical Center

Abstract Text:

Bayesian adaptive designs with response adaptive randomization (RAR) have the potential to benefit more participants in a clinical trial. While there are many papers that describe RAR designs and results, there is a scarcity of works reporting the details of RAR implementation from a statistical point exclusively. In this paper, we introduce the statistical methodology and implementation of the trial Changing the Default (CTD). CTD is a single-center prospective RAR comparative effectiveness trial to compare opt-in to opt-out tobacco treatment approaches for hospitalized patients. The design assumed an uninformative prior, conservative initial allocation ratio, and a higher threshold for stopping for success to protect results from statistical bias. A particular emerging concern of RAR designs is the possibility that time trends will occur during the implementation of a trial. If there is a time trend and the analytic plan does not prespecify an appropriate model, this could lead to a biased trial. Adjustment for time trend was not pre-specified in CTD, but post hoc time-adjusted analysis showed no presence of influential drift.

Keywords:

drift analysis |comparative effectiveness trial|Bayesian adaptive designs| | |

Sponsors:

Section on Bayesian Statistical Science

Tracks:

Applications in Life Sciences and Medicine

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