A Dermatological View—Interpreting Placebo Response in Clinical Trials for Psoriasis

Feb 1, 2013 | Contact Author | By: Howard I. Maibach, MD, University of California San Francisco; and Sonia Lamel, MD, University of Miami
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Title: A Dermatological View—Interpreting Placebo Response in Clinical Trials for Psoriasis
psoriasisx placebosx response ratesx random effectsx
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Keywords: psoriasis | placebos | response rates | random effects

Abstract: By comparing response rates of placebo versus active drug groups in psoriasis RCTs evaluating biologic agents, the authors of this column sought to clarify factors contributing to placebo responses and their implications in improving clinical trial design to determine more accurate drug efficacies.

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HI Maibach and S Lamel, A dermatological view—Interpreting placebo response in clinical trials for psoriasis, Cosm & Toil 128(2) 84-87 (Feb 2013)

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In clinical medicine, the randomized, placebo-controlled trial (RCT) is the main accepted method to demonstrate the efficacy of investigational topical skin care drugs, and is generally required in order to obtain U.S. Food and Drug Administration (FDA) approval. A similar but generally less rigorous methodology is adopted in the cosmetic industry for skin care trials. Volunteers in placebo treatment groups within RCTs often report considerable improvement; therefore, interpreting the efficacy results for those given the active is complicated by similar results from those given the placebo.

In relation, psoriasis is a long-lasting, immunologically mediated, often severe inflammatory skin disease affecting approximately 3% of the Caucasian population. A class of drugs known as the “biologics” is efficacious in treating psoriasis; however, even well-designed psoriasis RCTs can have significant placebo response rates. When response values are present for both the placebo group and the active group, the additive model predicts efficacy by subtracting the response of the placebo group from the active group. However, the observed placebo response may reflect factors not implicit in the general definition of placebo response. These indeterminate factors warrant further investigation to optimize RCT design.

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Table 1. Odds ratio of response determined by a random effects model (p < 0.0001)

Table 1. Odds ratio of response determined by a random effects model  (p < 0.0001)

The overall odds ratio calculated was 23.94 (p < 0.0001, 95% Confidence Interval (CI) 16.02–35.76).

Table 2. Multiple variable logistic regression model

Table 2. Multiple variable logistic regression model

The contribution of covariates to the rates of placebo responders

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