What You Need to Know About Adaptive Trials - Pharmaceutical Executive

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What You Need to Know About Adaptive Trials
Enabled by the power of today's computers, a handful of new statistical techniques and clinical-trial designs promise big benefits for pharma, doctors, and patients alike. They'll let you change the way you run your business—and they'll force you to change. Here's your guide to the basics.


Pharmaceutical Executive


STAGE ONE

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 as possible.

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.


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