Premier Voices #4: The Placebo Problem Part 2 With Michael Kuss

Measuring, interpreting, and mitigating placebo response is a persistent and growing challenge in analgesia clinical trials. In the conclusion of our Premier Voices podcast series on the placebo problem, Paul Mirek, Marketing Manager, and Michael Kuss, BS, Vice President, Analgesia, examine experimental trial designs, inclusion and exclusion criteria, and other approaches to managing the placebo effect.

Experimental designs have been shown to be very effective in measuring placebo response. For example, adding a third arm to a traditional parallel group design produces a study with active vs. placebo vs. no-treatment arms. In such a study, the no-treatment population receives only standard of care or no active treatment, and the placebo response is the difference between the placebo and no-treatment arms.

This approach works well in research studies but can unnecessarily complicate Phase III trials. Before adding a no-treatment arm, you should coordinate with your trial design team or CRO to make sure the strategy fits your needs.

Another experimental design option is a crossover trial, in which each patient is treated with the investigational drug and then crosses over to placebo, and vice versa. A-B vs. B-A randomization allows patients to serve as their own control, but there are limits to this method because, when receiving subsequent treatments, patients react to the medication they received previously.

Response conditioning and pharmacological conditioning, in which patients are conditioned with medication plus verbal and non-verbal cues, are other experimental approaches. Words and suggestion can be as powerful as drugs in triggering patient responses.

Data interpretation and the placebo response

Interpretation of data can also affect placebo response. That’s because the interaction between drugs and placebos remains an inexact science, and one we still don’t fully understand.

One way we interpret these interactions is through the additive method, by which the response in the drug group equals the sum of the placebo response and the drug’s pharmacological effect. This assumes that the placebo response is the same in the placebo group as in the drug group and that the pharmacological effect of the drug is unaffected by whether or not the patient thinks he or she has received the drug.

Another approach is the interactive method, which assumes unequal placebo responses in the treatment and placebo groups. It differs from the additive method in that we assume that the placebo response is equal in both treatment groups in an additive study, but that there may be some interplay between the active and placebo groups that will cause an unequal response to placebo.

There are also trial-independent factors that can affect the placebo response, including regression to the mean and patients’ natural tendency to get better.

Mitigating placebo response through inclusion and exclusion criteria

Adjusting inclusion and exclusion criteria is another way to mitigate the placebo problem. We can design what appears to be an ideal recruiting strategy, but that doesn’t necessarily mean the patients will be enrollable, or that they will be consistent with the general population. Ideally, the results of one study would be transferable to other studies or to the population at large.

Other tactics try to control for the duration of the disease. Patients who are newly diagnosed may experience spot remission, making them unsuitable for enrollment. Ideally, patients should have had the disease, or symptoms of the disease, for long enough that the condition has reached a stable state. Conversely, enrolling longtime sufferers may expose the trial to people who have been misdiagnosed or who are refractory to treatment, both of which could have negative effects.

There’s much more to the topic, such as selecting the best primary endpoint and why it’s generally preferable to use as few sites as possible. Check out Premier Voices with Paul Mirek and Michael Kuss for the full story.

Full presentation:


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