The simplest forms of adaptive trials are known as "staged" protocols or "group sequential" trials, and some of them are already
well known in clinical oncology and some other indications. The most familiar example is the "3+3" Phase I trial design for
finding a maximum-tolerated-dose (MTD). In a 3+3 trial, three patients start at a given dose, and if no dose-limiting toxic
effects are seen, three more patients are added to the trial at a higher dose. If there is one instance of limiting toxicity
in the first group, three more patients are added at the same dose. If two (or all three) in any cohort show dose-limiting
toxicity, the next lower dose is declared to be the maximum tolerated. There are many variations on this sort of trial, with
different numbers of stages and varying endpoints, but they tend to work in the same framework, no matter which clinical phase
is being addressed. Generally, as with this MTD example, the decision that's being addressed at each stage is whether to continue
or stop the study.
Essentially, group-sequential trials are fragmented versions of the classic trial design, giving the investigators more opportunities
for decision points rather than waiting to see the whole picture at the end. Helpful as that is in theory, in practice, there
are some concerns. One particularly important issue: The endpoints and number of patients at each stage have to be chosen
to ensure that there is sufficient statistical power to actually answer the questions the trial is supposed to answer at each
stage. For example, the sample size in the first stage needs to be large enough to give a low probability of a false-negative
result—halting the trial of a compound that was actually efficacious. On the other hand, it is important to guarantee that
the number of patients in the control groups is large enough to ensure that non-efficacious trials terminate swiftly, especially
in later stages. In fact, it is well known that simple staged designs like the 3+3 are statistically underwhelming: They persist
mainly because there's no consensus on what to replace them with.
Part of this problem is built into the very nature of staged trials: When trials are conducted sequentially, the false-positive
and false-negative rates for each stage of a study inevitably grow. When a trial is broken into three parts, for example,
the chances for a false positive readout can more than double. In 1989, Richard Simon of the National Cancer Institute proposed
templates for staged trials that maximized the power for both positive and negative determinations, but the patient requirements
on each side can be rather different—and sometimes mutually exclusive. Compromises are common, and several graphical and numerical
methods have recently been proposed for finding designs that minimize sample sizes while maintaining as much statistical potency
INTO THE BAYESIAN UNIVERSE
This whole question of trial design and statistical power illustrates a fundamental issue with staged trials: When you conduct
a trial using a classic (frequentist) statistical approach, you only have so much maneuvering room, and there can be limits
to the interpretation of the data as well.
Bill Gillespie of Pharsight, a clinical consulting firm, points out that the hypothesis-testing mode of frequentist statistics
still leaves the eventual decision-making in a binary state: "You set up a null hypothesis and hope to reject it. If you do,
you end up with a fairly strong statement that you're better than nothing, but you don't know how much better you are." The
data collection often has to be done in a binary mode as well, classifying patients, for example, as responders or non-responders
according to pre-set criteria. In many cases, a more finely tuned readout would be helpful.
Such issues are why the quest for more complex and powerful trial designs leads, in many cases, to the alternate universe
of Bayesian statistics. Gillespie is an advocate of this approach in adaptive clinical trials, which he says allows for much
more flexibility. It's important to remember that the word "adaptive" isn't always a synonym for "Bayesian," but in moving
to higher-level adaptive designs, the topic will always come up, since many of these designs are indeed easier to deal with
in a Bayesian framework.