February 28, 2023

Placebo-controlled clinical trials are the gold standard in drug development, in part to ensure that the efficacy of a new therapy exceeds the placebo response in the indication being studied. The placebo response is a measured improvement in clinical signs or symptoms that occurs in patients receiving a sham (or “dummy”) treatment. The placebo response is a complex psychological, biological and sociological phenomenon that confounds clinical data analysis, particularly for subjective patient-reported outcomes. The placebo response is widely known to compromise evaluation of pain endpoints and has been suggested to contribute to as much as ~2/3 of the measured treatment effect in pain from various etiologies (1), contributing to the high rate of Phase II and III clinical trial failure in this indication (2). While the placebo response is not limited to pain trials, there is a unique impact of this phenomenon in pain. This article is the fifth in a series that examines the impact of the placebo response in drug development in pain and beyond. 

The International Association for the Study of Pain (IASPdefines pain as “an unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage.” At least 100 million American adults live with chronic pain every year (3) – ranging from musculoskeletal pain to migraines. Chronic low back pain is, in fact, the leading cause of disability in most countries in the world (4).

However, the attempt to understand pain is one of the oldest challenges in medicine (5) for three primary reasons: lack of disease status, variable pain reporting, and the placebo response. 

Despite its global prevalence and burden, chronic pain has not always been recognized as a disease – making it more difficult for researchers to classify and assess pain in clinical development, and for patients to access multimodal pain management. In 2019, the World Health Organization (WHO) adopted a classification system for chronic pain in collaboration with an IASP Working Group and the International Classification of Diseases (ICD). This improvement in classification and diagnostic coding advanced the recognition of chronic pain as a disease (6), thereby shifting attention to the way pain is assessed in clinical development.

Pain as a disease is highly complex and variable, adding to the challenge of getting new therapies to market. There are many pain etiologies, as well as variable reporting of pain by patients. Each patient in a clinical trial has their own pain tolerance and perception of pain intensity, and it can be difficult to standardize this perception of pain intensity from day to day.

Moreover, the placebo response underpins the above challenges, making it difficult to determine efficacy and safety of new drugs. In peripheral neuropathic pain (PNP), for example, the placebo response alone may account for as much as 60% of the analgesic response (1). In osteoarthritis (OA), the placebo response accounts for as much as 83% of stiffness reduction and 75% of pain reduction (7). The source of this response is still being understood, but the placebo effect – one component of the overall placebo response – is linked to psychoneurobiological changes occurring as a result of the patient’s previous experience and own or induced expectations (8). Additionally, verbal suggestions, classical and nonclassical conditioning, and social interactions (including observation and complex interpersonal interactions) can trigger placebo effects (8). Ultimately, the expectation of symptom improvement is the top underlying mechanism of a patient’s placebo response (9). 

A review of phase II and phase III failures from 2013-2015 pointed to efficacy and safety as the top reasons for trial failures (10) – and the placebo response is the likely culprit for creating such noise that makes efficacy and safety difficult to prove. Because of this, the industry has long worked toward reducing the placebo response to improve assay sensitivity and accelerate drug development (11). The Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) consortium examined several areas to increase outcome measurement sensitivity in chronic pain studies (12, 13). They provided evidence-based recommendations for core phenotyping domain, with recommended measures for each. One such domain included psychosocial, an area that has long been unaddressed by traditional methods of mitigating placebo response. This is because intrinsic factors of the placebo response – like the placebo effect – is closely linked to the patient and their psychology, a domain that has been historically difficult to quantify. However, the advent of AI-based methods has allowed researchers to mitigate the negative consequences of high placebo-response rates (14) by way of a covariate adjustment (15). The use of such prognostic covariates are defined by the FDA (16) as a way to improve the estimation of the treatment effect. It is well-known that in case of a strong or moderate association between a baseline covariate(s) and the primary outcome measure, adjustment for such covariate(s) may improve the efficiency of the analysis as described by the EMA (17). 

This is the approach that Cognivia has used to develop our platform solution, Placebell©™. Following a sophisticated evaluation of patient characteristics including psychology, Placebell generates a prediction of each patient’s placebo responsiveness. Applying Placebell©™ in a clinical trial statistical analysis results in increased study power, improved p-values and a reduced risk of trial failure. Placebell©™ has been successfully applied in areas like painosteoarthritis and Parkinson’s disease, and is applicable to virtually any disease. To learn more about how Placebell©™ could be applied in pain, contact us.


1. Häuser W, Bartram-Wunn E, Bartram C, Reinecke H, Tölle T. Systematic review: Placebo response in drug trials of fibromyalgia syndrome and painful peripheral diabetic neuropathy – Magnitude and patient-related predictors. Pain. 2011;152(8):1709-1717. doi:10.1016/j.pain.2011.01.050

2. Dumitrescu TP, McCune J, Schmith V. Is Placebo Response Responsible for Many Phase III Failures? Clin Pharmacol Ther. 2019;106(6):1151-1154. doi:10.1002/cpt.1632

3. Kaptchuk T J, Hemond C C, Miller F G. Placebos in chronic pain: evidence, theory, ethics, and use in clinical practice BMJ 2020; 370 :m1668 doi:10.1136/bmj.m1668

4. GBD 2016 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017;390:1211-59. doi:10.1016/S0140-6736(17)32154-2

5. Raffaeli W, Arnaudo E. Pain as a disease: an overview. J Pain Res. 2017 Aug 21;10:2003-2008. doi: 10.2147/JPR.S138864. PMID: 28860855; PMCID: PMC5573040.

6. Treede RD, Rief W, Barke A, Aziz Q, Bennett MI, Benoliel R, Cohen M, Evers S, Finnerup NB, First MB, Giamberardino MA, Kaasa S, Korwisi B, Kosek E, Lavand’homme P, Nicholas M, Perrot S, Scholz J, Schug S, Smith BH, Svensson P, Vlaeyen JWS, Wang SJ. Chronic pain as a symptom or a disease: the IASP Classification of Chronic Pain for the International Classification of Diseases (ICD-11). Pain. 2019 Jan;160(1):19-27. doi: 10.1097/j.pain.0000000000001384. PMID: 30586067.

7. Zhang W. The powerful placebo effect in osteoarthritis. Clin Exp Rheumatol. 2019 Sep-Oct;37 Suppl 120(5):118-123. Epub 2019 Oct 15. PMID: 31621561.

8. Colloca L. The Placebo Effect in Pain Therapies. Annu Rev Pharmacol Toxicol. 2019 Jan 6;59:191-211. doi: 10.1146/annurev-pharmtox-010818-021542. Epub 2018 Sep 14. PMID: 30216744; PMCID: PMC6402571.

9. Holmes RD, Tiwari AK, Kennedy JL. Mechanisms of the placebo effect in pain and psychiatric disorders. Pharmacogenomics J. 2016 Nov;16(6):491-500. doi: 10.1038/tpj.2016.15. Epub 2016 Mar 22. PMID: 27001122.

10. Harrison RK. Phase II and phase III failures: 2013-2015. Nat Rev Drug Discov. 2016 Dec;15(12):817-818. doi: 10.1038/nrd.2016.184. Epub 2016 Nov 4. PMID: 27811931.

11. Evans K, Colloca L, Pecina M, Katz N. What can be done to control the placebo response in clinical trials? A narrative review. Contemp Clin Trials. 2021 Aug;107:106503. doi: 10.1016/j.cct.2021.106503. Epub 2021 Jul 6. PMID: 34237458; PMCID: PMC8719632.

12. Dworkin RH, Turk DC, Peirce-Sandner S, Burke LB, Farrar JT, Gilron I, Jensen MP, Katz NP, Raja SN, Rappaport BA, Rowbotham MC, Backonja MM, Baron R, Bellamy N, Bhagwagar Z, Costello A, Cowan P, Fang WC, Hertz S, Jay GW, Junor R, Kerns RD, Kerwin R, Kopecky EA, Lissin D, Malamut R, Markman JD, McDermott MP, Munera C, Porter L, Rauschkolb C, Rice ASC, Sampaio C, Skljarevski V, Sommerville K, Stacey BR, Steigerwald I, Tobias J, Trentacosti AM, Wasan AD, Wells GA, Williams J, Witter J, Ziegler D. Considerations for improving assay sensitivity in chronic pain clinical trials: IMMPACT recommendations. Pain. 2012 Jun;153(6):1148-1158. doi: 10.1016/j.pain.2012.03.003. Epub 2012 Apr 9. PMID: 22494920.

13. Edwards RR, Dworkin RH, Turk DC, Angst MS, Dionne R, Freeman R, Hansson P, Haroutounian S, Arendt-Nielsen L, Attal N, Baron R, Brell J, Bujanover S, Burke LB, Carr D, Chappell AS, Cowan P, Etropolski M, Fillingim RB, Gewandter JS, Katz NP, Kopecky EA, Markman JD, Nomikos G, Porter L, Rappaport BA, Rice ASC, Scavone JM, Scholz J, Simon LS, Smith SM, Tobias J, Tockarshewsky T, Veasley C, Versavel M, Wasan AD, Wen W, Yarnitsky D. Patient phenotyping in clinical trials of chronic pain treatments: IMMPACT recommendations. Pain. 2016 Sep;157(9):1851-1871. doi: 10.1097/j.pain.0000000000000602. PMID: 27152687; PMCID: PMC5965275.

14. Smith EA, Horan WP, Demolle D, Schueler P, Fu DJ, Anderson AE, Geraci J, Butlen-Ducuing F, Link J, Khin NA, Morlock R, Alphs LD. Using Artificial Intelligence-based Methods to Address the Placebo Response in Clinical Trials. Innov Clin Neurosci. 2022 Jan-Mar;19(1-3):60-70. PMID: 35382067; PMCID: PMC8970233.

15. Branders S, Pereira A, Bernard G, Ernst M, Dananberg J, Albert A. Leveraging historical data to optimize the number of covariates and their explained variance in the analysis of randomized clinical trials. Stat Methods Med Res. 2022 Feb;31(2):240-252. doi: 10.1177/09622802211065246. Epub 2021 Dec 13. PMID: 34903096.



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