Therapeutic confirmatory trials objectives

Table 10.1 Taxonomy of therapeutic confirmatory trial objectives...

Safety analyses are not typically prespecified in the study protocol and/or the study analysis plan. Studies are typically powered on efficacy outcomes (the primary objective in therapeutic confirmatory trials see Chapter 9), and the sample size that results from this sample-size estimation may be considerably smaller than would be needed for a thorough investigation of safety data. 

All of the studies that are performed before a therapeutic confirmatory trial is started collect information that facilitates a logical scientific progression from FTIH studies to the point where the therapeutic confirmatory trial is appropriate. In a real sense, all of these studies, and all of the information gained to date, have had one purpose to allow the primary objective in the therapeutic confirmatory trial to be stated as simply as possible. In this context, the word simple is not pejorative. To the contrary, a primary objective that can be stated simply can be tested simply, i.e., in a straightforward and unambiguous manner. This is a highly desirable attribute in a primary objective. 

By the time a therapeutic confirmatory trial is appropriate, it should be possible to state a single primary objective (or perhaps two if the sponsor really feels that this is appropriate) that is clinically relevant and biologically plausible. One primary objective also means that sample-size estimation can be based on that objective and the associated estimated treatment effect of interest (recall the discussions in Chapter 9). 

It is also appropriate to note that not all clinical trials utilize formal sample size estimation methods. In many instances (for example, FTIH studies) the sample size is determined on the basis of logistical constraints and the size of the study thought to be necessary to gather sufficient evidence (for example, pharmacokinetic profiles) to rule out unwanted effects. However, when the objective of the clinical trial (for example, a superiority trial) is to claim that a true treatment effect exists while at the same time limiting the probability of committing type I or II errors (a and P), there are computational methods used to estimate the required sample size. The use of formal sample size estimation is required in therapeutic confirmatory trials, this book s major focus, and strongly suggested in therapeutic exploratory trials. 

---