A meta-analysis of open-label placebos in clinical trials

Open-label placebos (OLPs) are placebos without deception in the sense that patients know that they are receiving an inert sugar pill with no activity of its own. Intuitively, we think that such treatments must be ineffective. Yet, there have been several studies that seemed to show otherwise.

The objective of this paper was to systematically review and analyze the effect of OLPs in comparison to no treatment in clinical trials. A systematic literature search was carried out in February 2020. Randomized controlled trials of any medical condition or mental disorder comparing OLPs to no treatment were included. Data extraction and risk of bias rating were independently assessed. 1246 records were screened and 13 studies were included in the systematic review. Eleven trials were eligible for meta-analysis.

These trials assessed the effects of OLPs on

• back pain,
• cancer-related fatigue,
• attention deficit hyperactivity disorder,
• allergic rhinitis,
• major depression,
• irritable bowel syndrome,
• menopausal hot flushes.

The risk of bias was moderate among all studies.

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A significant overall effect (standardized mean difference = 0.72, 95% Cl 0.39–1.05, p < 0.0001, I² = 76%) of OLP. Thus, OLPs appear to be a promising treatment in different conditions. Yet, the researchers spotted several caveats and discuss them in some detail.

First, we detected hints of a publication bias in the study sample, but the respective test was not significant. The quantitative basis of the meta-analysis is based on a small number of studies, reflecting the early state of research in this field. Moreover, the set of studies showed some heterogeneity. Finally, four studies were rated to have a high risk of bias, and nine to have some concerns.

In order to assess the impact of these high-risk studies we performed an exploratory best-evidence synthesis. We excluded the four studies with a high risk of bias. In this analysis, the heterogeneity could be reduced to a non-critical value and almost all variance in the set of studies could be explained by a sampling error (I² = 4%). With the exclusion of these four studies the mean effect size was reduced to a more conservative SMD = 0.49.

Regardless of this reduction of the overall effect, the same conclusions about the treatment-effect of OLPs can be drawn, although the lack of robustness means that interpretations require some caution. The decrease of heterogeneity shows that methodological impairments might be responsible for the considerable unexplained variance in our results. We abstained from carrying out a further sensitivity analysis for explaining heterogeneity because of the small number of studies.

This is certainly an interesting subject. And the above findings are certainly counter-intuitive.

My impression is that the effect of OLPs is small and of doubtful value in clinical practice. My prediction is that, as more and better research emerges, it will diminish further, if not vanish totally. I think that there are several reasons for this:

• The number of trials is still quite small.
• The studies obviously lack patient blinding.
• Positive messages can be included alongside open-label placebos.
• The “time lag bias” is high.

This type of bias means that, due to initial enthusiasm in a new subject, negative results are published with some delay. I have observed this bias repeatedly in the past. A new treatment initially tends to generate nothing but positive results, and only after a while, when the researchers’ euphoria has subsided, more realistic findings emerge.