Developing Better Clinical Trials
Although double blind randomized controlled trials (DBRCTs) are considered the gold standard in evaluating a drug or therapy, it may be time for a new model, according to researchers. They say that DBRCTs don’t factor in patient behaviors, such as diet and lifestyle choices, that can affect the drug or treatment being tested.
A recent analysis of six DBRCTs, led by Caltech’s Erik Snowberg, professor of economics and political science, and his colleagues Sylvain Chassang from Princeton University and Ben Seymour from Cambridge University, shows that behavior can have a serious impact on the effectiveness of a treatment. As a result, the researchers proposed a new trial design, called a two-by-two trial, that can account for behavior–treatment interactions.
The study was published in the journal PLOS ONE.
According to a news release from Caltech, patients behave in different ways during a trial. These behaviors can directly relate to the trial—for example, one patient who believes in the drug may religiously stick to his or her treatment regimen while someone more skeptical might skip a few doses. The behaviors may also simply relate to how the person acts in general, such as preferences in diet, exercise, and social engagement. And in the current design of trials, these behaviors are not accounted for, Snowberg says.
For example, a DBRCT might randomly assign patients to one of two groups: an experimental group that receives the new treatment and a control group that does not. As the trial is double-blinded, neither the subjects nor their doctors know who falls into which group. This is intended to reduce bias from the behavior and beliefs of the patient and the doctor; the thinking is that because patients have not been specifically selected for treatment, any effects on health outcomes must be solely due to the treatment or lack of treatment.
But, Snowberg says, although the patients do not know whether they have received the treatment, they do know their probability of getting the treatment—in this case, 50 percent. And a 50 percent likelihood of getting the new treatment might not be enough to encourage a patient to change behaviors that could influence the efficacy of the drug under study.
“Most medical research just wants to know if a drug will work or not. We wanted to go a step further, designing new trials that would take into account the way people behave. As social scientists, we naturally turned to the mathematical tools of formal social science to do this,” Snowberg says.
In the two-by-two trial developed by the researchers, instead of patients first being assigned to either the experimental or control groups, they are randomly assigned to either a “high probability of treatment” group or a “low probability of treatment” group. The patients in the high probability group are then randomly assigned to either the treatment or the control group, giving them a 70 percent chance of receiving the treatment. Patients in the low probability group are also randomly assigned to treatment or control; their likelihood of receiving the treatment is 30 percent. The patients are then informed of their probability of treatment.
By randomizing both the treatment and the probability of treatment, medical researchers can quantify the effects of treatment, the effects of behavior, and the effects of the interaction between treatment and behavior. Determining each, Snowberg says, is essential for understanding the overall efficacy of treatment.