2020/10/01 by Brian D. Segal, Segal, Brian D., Katherine Tan +1
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Health Systems, Economic Evaluations, Quality of Life #Methodology (stat.ME) #Statistical Methods and Inference #Statistical Methods in Clinical Trials
paper · pdf · doi:10.48550/arxiv.2010.00433
openalex publication_date 2020/10/01 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
In situations where it is difficult to enroll patients in randomized\ncontrolled trials, external data can improve efficiency and feasibility. In\nsuch cases, adaptive trial designs could be used to decrease enrollment in the\ncontrol arm of the trial by updating the randomization ratio at the interim\nanalysis. Updating the randomization ratio requires an estimate of the amount\nof information effectively borrowed from external data, which is typically done\nwith a linear approximation. However, this linear approximation is not always a\nreliable estimate, which could potentially lead to sub-optimal randomization\nratio updates. In this note, we highlight this issue through simulations for\nexponential time-to-event outcomes, because in this simple setting there is an\nexact solution available for comparison. We also propose a potential\ngeneralization that could complement the linear approximation in more complex\nsettings, discuss challenges for this generalization, and recommend best\npractices for computing and interpreting estimates of the effective number of\nevents borrowed.\n