By Richard Chin

Adaptive medical trial designs, not like conventional mounted medical trial designs, allow amendment of reviews according to the knowledge generated throughout the trial. This frequently leads to experiences which are considerably quicker, extra effective, and extra robust. contemporary advancements in web-based real-time info access and advances in statistical tools have made adaptive scientific trials even more popular Read more...

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Some of these limitations should eventually become less problematic as clinical trialists, regulatory agencies, ethics committees, and sponsors/site personnel gain more experience with adaptive studies and as methods to address the issues are developed. 22 Adaptive and Flexible Clinical Trials The first risk is that because logistic, statistical, and operational processes for adaptive clinical trials are new, there might be unanticipated mistakes and errors in the operational aspects of the trial.

Therefore a clinical trial design should maximize the likelihood of positive study results. A study that is well designed, in addition to yielding accurate results, also poses the right question. For example, a study looking at the impact on 30-day mortality of 100 mg of a certain drug in severe sepsis patients may yield an accurate result that it has no impact. However, it may be that 200 mg of the drug in moderate sepsis patients has a beneficial impact on 60-day mortality. Selecting the right patient population, the right dose, and the right endpoint has a critical impact on the likelihood of success.

They are much more efficient than conventional trials. They also tend to lower cost and timelines significantly. With regard to feasibility, however, adaptive trials are more difficult to conduct well and are relatively new. Apart from the above performance criteria, there is an important fundamental question about adaptive designs that is not yet fully resolved. Adaptive designs can be powerful, but the statistical techniques are still in the process of being developed. The demands being placed on conventional statistical techniques are straining the conventional frequentist statistical methods to the breaking point.

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