New trial design accounts for patient behavior

“It’s a very small change to the design of the trial, but it’s important,” he said. “The effect of a treatment has these two constituent parts: pure treatment effect and the treatment-behavior interaction. Standard blind trials just randomize the likelihood of treatment, so you can’t see this interaction. Although you can’t just tell someone to randomize their behavior, we found a way that you can randomize the probability that a patient will get something that will change their behavior.”

Testing the design

Because it is difficult to implement new trial design changes in active trials, Dr Snowberg and his colleagues wanted to first test their idea with a meta-analysis of data from previous clinical trials. They devised a new mathematical formula that can be used to analyze DBRCT data.

The formula teases out the health outcomes resulting from treatment alone as well as outcomes resulting from an interaction between treatment and behavior.

The investigators used the formula to analyze 6 DBRCTs evaluating the antidepressants imipramine (a tricyclic antidepressant also known as Tofranil) and paroxetine (a selective serotonin reuptake inhibitor sold as Paxil).

First, the researchers wanted to see if there was evidence that patients behave differently when they have a high probability of treatment and when they have a low probability of treatment.

The trials recorded how many patients dropped out of the study, so this was the behavior Dr Snowberg and his colleagues analyzed. They found that, in trials where patients happened to have a relatively high probability of treatment—near 70%—the dropout rate was significantly lower than in trials where patients had a lower probability of treatment—around 50%.

Although the team did not have any specific behaviors to analyze, other than dropping out of the study, they also wanted to determine if behavior in general could have added to the effect of the treatments.

Using their statistical techniques, the investigators found that imipramine seemed to have a pure treatment effect but no effect from the interaction between treatment and behavior. That is, the drug seemed to work fine, regardless of any behavioral differences that may have been present.

The researchers also found that paroxetine seemed to have no effect from the treatment alone or behavior alone. However, an interaction between the treatment and behavior did effectively decrease depression.

Because this study was conducted in the past, the investigators could not determine which specific behavior was responsible for the interaction, but with the mathematical formula, they were able to tell that this behavior was necessary for the drug to be effective.

In their paper, Dr Snowberg and his colleagues speculate how a situation like this might come about.

“Maybe there is a drug, for instance, that makes people feel better in social situations, and if you’re in the high probability group, then maybe you’d be more willing to go out to parties to see if the drug helps you talk to people,” Dr Snowberg explained.

“Your behavior drives you to go to the party, and once you’re at the party, the drug helps you feel comfortable talking to people. That would be an example of an interaction effect. You couldn’t get that if people just took this drug alone at home.”

Although this specific example is just speculation, Dr Snowberg said the researchers’ actual results reveal that some behavior or set of behaviors interact with paroxetine to effectively treat depression. And, without this behavior, the drug appears to be ineffective.

“Normally, what you get when you run a standard blind trial is some sort of mishmash of the treatment effect and the treatment-behavior interaction effect,” Dr Snowberg said. “But knowing the full interaction effect is important.”