David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Journal of Experimental and Theoretical Artificial Intelligence 24 (4):513-526 (2012)
Despite efforts from regulatory agencies (e.g. NIH, FDA), recent systematic reviews of randomised controlled trials (RCTs) show that top medical journals continue to publish trials without requiring authors to report details for readers to evaluate early stopping decisions carefully. This article presents a systematic way of modelling and simulating interim monitoring decisions of RCTs. By taking an approach that is both general and rigorous, the proposed framework models and evaluates early stopping decisions of RCTs based on a clear and consistent set of criteria. The framework allows decision analysts to generate and quickly answer ‘what-if’ questions by simulating alternate trial scenarios. I illustrate the framework with a case study of an RCT that was stopped early due to harm. This was a trial of vitamin A supplement in relation to HIV transmission from mother-to-child through breastfeeding.
|Keywords||computer modeling and simulation statistical evidence and inference decision theory counterfactual reasoning Bayesian methods randomized controlled trials technology|
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Citations of this work BETA
Roger Stanev (2015). Early Stopping of RCTs: Two Potential Issues for Error Statistics. Synthese 192 (4):1089-1116.
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