The task set before clinical investigators is not easy. They are supposed to answer pressing scientific questions, using very few resources, and exposing patient-subjects to as little risk as possible. In other words, we expect them to be rigorous scientists, stewards of the medical research enterprise, and guardians of their patients’ interests all at the same time. While the duties that emerge from these various roles are sometimes orthogonal, they are intersecting and aligned at the point of clinical trial design. Insofar as a trial is well-designed–meaning that it is likely to answer its scientific question, make efficient use of research resources, and minimize risk–the investigator has successfully discharged all of these duties.What is more, there is a common activity underlying all of these requirements of good trial design: Prediction. When investigators design studies, they are making an array of predictions about what they think will happen. When they decide which interventions to compare in a randomized trial, they are making predictions about risk/benefit balance. When they power a study, they are making a prediction about treatment effect sizes. The accuracy of these predictions can mean the difference between an informative or an uninformative outcome–a safe or unsafe study.
The importance of these predictions is already implicitly recognized in many research ethics policies. Indeed, research policies often include requirements that studies should be based on a systematic evaluation of the available evidence. These requirements are really just another way of saying that the predictions underlying a study should be as accurate as possible given the state of available knowledge.
The views, opinions and positions expressed by these authors and blogs are theirs and do not necessarily represent that of the Bioethics Research Library and Kennedy Institute of Ethics or Georgetown University.