Abstract Number:
1711
Submission Type:
Topic-Contributed Paper Session
Participants:
Judith Lok (1), Guangyu Tong (2), Donna Spiegelman (3), Christopher Harshaw (4), Bingkai Wang (5), Ambarish Chattopadhyay (6), Judith Lok (7)
Institutions:
(1) Boston University, N/A, (2) Yale University, N/A, (3) Yale School of Public Health, N/A, (4) MIT and UC Berkeley, N/A, (5) Wharton School, University of Pennsylvania, N/A, (6) Stanford University, N/A, (7) Boston University and Harvard University, N/A
Chair:
Session Organizer:
Speaker(s):
Judith Lok
Boston University and Harvard University
Session Description:
Even when executed well, it is not rare for confirmatory randomized trials to conclude there is insufficient evidence to detect a causal effect of the intervention compared to control. Recently, several statistical methods are being developed to avoid such failed trials. For example, as in Type 2 Hybrid effectiveness implementation designs, effectiveness and implementation are tested and assessed simultaneously. Or, the treatment allocation can be adaptively adjusted over the course of the trial. Or, covariate adjustment can be used to increase power without jeopardizing robustness: the type-1 error is not increased even if the adjustment model assumptions are not met. Or, as in SMART or Sequential Multiple Assignment Randomized Trials, Micro Randomized Trials, and Hybrid Experimental Designs, the intervention is randomized over time depending on a patient's own past outcomes. Or, as in LAGO or Learn-As-you-GO designs, the intervention component composition of a multi-component intervention is optimized while the trial is ongoing, and data from all phases is used to both test the null hypothesis of no intervention effect and to estimate the optimal intervention and its effect. All these strategies have a common goal: to avoid failed trials and find effective interventions.
Sponsors:
Biopharmaceutical Section 3
Caucus for Women in Statistics 2
ENAR 1
Theme:
Statistics and Data Science: Informing Policy and Countering Misinformation
Yes
Applied
Yes
Estimated Audience Size
Medium (80-150)
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