This is the tenth installment of our look at the increasingly high placebo response that is plaguing clinical trials in analgesia and psychiatry. Catch up on the other posts in the series here.
Over the last two weeks we’ve discussed several specific strategies to reduce the placebo response. One effort that is becoming increasingly popular is the manipulation of expectancy. Patient and clinician expectancy of improvement is a major contributor to the placebo response (for more details, see our third post in this series), and decreasing expectations is an effective placebo response reduction strategy. Today we turn to the long list of specific trial-related factors such as pill color and placebo type that can be used to manipulate expectancy, many of which are quite subtle and have already been exploited in the strategies we discussed last week.
A number of pill characteristics influence the magnitude of the placebo response: bigger pills are more effective than smaller ones, capsules are more effective than tablets, and having a name printed on the pill also boosts its perceived efficacy. The dosing schedule, too, plays a role: placebos given more often elicit a bigger response than ones with a single administration.
Of all the pill characteristics, however, color seems to be the most important. In general, colored pills work better than white ones, and brightly colored pills even seem to be superior to those that have a dull color. But the specific color can also impact patient expectancy, both in terms of the drug’s perceived action and its perceived effectiveness. Red, yellow, and orange pills are judged as more likely to be stimulants, while subjects feel blue and green pills are more likely to be tranquilizers. In addition, drugs seem to work best when their color matches the goal. So, a white burn cream works better than a red one.
The color of a drug’s packaging, too, can also modulate patient expectancy. Darker shades of packaging, such as brown and red, are rated as indicating the drug is for a more severe illness, and will have a more potent effect, than drugs in lighter packaging such as green and yellow. Red packages are also most associated with being treatments for the heart and pain; yellows, with treatments for the skin and heart; and green, with pain and liver. Blue and grey packages are also associated with the treatment of pain.
Different colors have different implications in different cultures, so it’s perhaps not surprisingly that the interpretation of pill color differs across cultures. In one study, Caucasians viewed white capsules as analgesics, while African Americans viewed them as stimulants. The reverse was true for black capsules: Caucasians saw them as stimulants; African Americans thought they were analgesics. Culture can also affect the way many other trial details are perceived, so it’s worth being mindful of the culture(s) of potential participants in the early trial design stage.
The exact type of placebo seems to matter a lot, too. In general, sham surgery seems to be the most effective, followed by sham injection, and then an oral placebo. For example, a systematic review of placebo treatments in 79 migraine prophylaxis trials found that 58 percent of individuals receiving a sham surgery responded (defined as a greater than 50 percent reduction in migraines) and 38 percent of people receiving sham acupuncture responded. Yet only 22 percent of those receiving a placebo pill experienced an effect. The finding that sham injections work better than oral placebos has also been replicated in other medical conditions. One study also demonstrated that a placebo laser treatment generated greater somatic sensations among participants than a placebo irritant solution.
Clinician attitudes and mannerisms
A number of characteristics of a trial’s medical practitioners also influence the magnitude of the placebo response. The clinician’s reputation is one important contributor. In addition, tone of voice, body language, eye contact, and other subtle cues can all influence the patient’s perception of the amount of attention, interest, and the overall level of concern the clinician is showing. Participants who feel like they are being cared for are more likely to exhibit a greater placebo response than those who rate their clinician as uncaring.
And, of course, what is said to the patient matters a lot. A verbal suggestion that the treatment will make the patient feel better increases his or her expectation of improvement, which in turn leads to a greater placebo response. In contrast, referring to the likelihood of improvement as possible rather than probable, can limit the placebo response.
Elements of the treatment context that make participants feel like they are being given a “medical treatment” – such as being in a medical setting like a doctor’s office, seeing the doctor’s white coat and other symbols of medicine, and feeling the needle stick – can also lead to an increased placebo response, though here again is an area where a patient’s culture can influence the meaning they ascribe to these details.
Finally, factors that make a patient think they are in a treatment group compared to a placebo group also affect the magnitude of the placebo response. For example, as the number of treatment arms increases, the placebo response increases because participants assume they have a greater chance of receiving the experimental treatment over the placebo and therefore getting better. In fact, in one study of Parkinson’s disease that compared transplantation of embryonic dopamine neurons to sham surgery, the patient’s perceived treatment group, and not their actual group, was the biggest predictor of improvement.
When it comes to controlling patient expectancy as a tool to limit the placebo response, it’s clear that details matter. That’s why it’s important to choose a CRO with experience in navigating these difficult waters.
Join us next week as we discuss the trial-independent factors, unfortunately outside of our control, that can contribute to the placebo response: the tendency of diseases to improve over time and the statistical phenomenon known as regression to the mean.