The placebome.

Placebo and its evil twin, nocebo

Placebo response in patients assigned active drug is a troubling source of variance in antidepressant studies. This paper summarizes a series of studies utilizing pattern analysis to distinguish between placebo and true drug responses. Analysis of the persistence, speed, and timing of onset of patients' improvement during antidepressant therapy reveals distinct patterns of response which are likely to be attributable to placebo and true drug effects. While true drug effects seem to be characterized by a 2-week delay in onset followed by persistent improvement, placebo effects seem to be characterized by abrupt, transient improvement. Gradual responses on placebo may be due to spontaneous remission. The heuristic and clinical implications of pattern analysis are discussed.

BackgroundAssumptions underlying placebo controlled trials include that the placebo effect impacts on all study arms equally, and that treatment effects are additional to the placebo effect. However, these assumptions have recently been challenged, and different mechanisms may potentially be operating in the placebo and treatment arms. The objective of the current study was to explore the nature of placebo versus pharmacological effects by comparing predictors of the placebo response with predictors of the treatment response in a randomised, placebo-controlled trial of a phytotherapeutic combination for the treatment of menopausal symptoms. A substantial placebo response was observed but no significant difference in efficacy between the two arms.MethodsA post hoc analysis was conducted on data from 93 participants who completed this previously published study. Variables at baseline were investigated as potential predictors of the response on any of the endpoints of flushing, overall menopausal symptoms and depression. Focused tests were conducted using hierarchical linear regression analyses. Based on these findings, analyses were conducted for both groups separately. These findings are discussed in relation to existing literature on placebo effects.ResultsDistinct differences in predictors were observed between the placebo and active groups. A significant difference was found for study entry anxiety, and Greene Climacteric Scale (GCS) scores, on all three endpoints. Attitude to menopause was found to differ significantly between the two groups for GCS scores. Examination of the individual arms found anxiety at study entry to predict placebo response on all three outcome measures individually. In contrast, low anxiety was significantly associated with improvement in the active treatment group. None of the variables found to predict the placebo response was relevant to the treatment arm.ConclusionThis study was a post hoc analysis of predictors of the placebo versus treatment response. Whilst this study does not explore neurobiological mechanisms, these observations are consistent with the hypotheses that 'drug' effects and placebo effects are not necessarily additive, and that mutually exclusive mechanisms may be operating in the two arms. The need for more research in the area of mechanisms and mediators of placebo versus active responses is supported.Trial RegistrationInternational Clinical Trials Registry ISRCTN98972974.

The nature and magnitude of placebo and nocebo responses to ADHD medications and the extent to which response to active medications and placebo are inter-correlated is unclear. To assess the magnitude of placebo and nocebo responses to ADHD and their association with active treatment response. We searched literature until June 26, 2019, for published/unpublished double-blind, randomised placebo-controlled trials (RCTs) of ADHD medication. Authors were contacted for additional data. We assessed placebo effects on efficacy and nocebo effects on tolerability using random effects meta-analysis. We assessed the association of study design and patient features with placebo/nocebo response. We analysed 128 RCTs (10,578 children/adolescents and 9175 adults) and found significant and heterogenous placebo effects for all efficacy outcomes, with no publication bias. The placebo effect was greatest for clinician compared with other raters. We found nocebo effects on tolerability outcomes. Efficacy outcomes from most raters showed significant positive correlations between the baseline to endpoint placebo effects and the baseline to endpoint drug effects. Placebo and nocebo effects did not differ among drugs. Baseline severity and type of rating scale influenced the findings. Shared non-specific factors influence response to both placebo and active medication. Although ADHD medications are superior to placebo, and placebo treatment in clinical practice is not feasible, clinicians should attempt to incorporate factors associated with placebo effects into clinical care. Future studies should explore how such effects influence response to medication treatment. Upon publication, data will be available in Mendeley Data: PROSPERO (CRD42019130292).

BackgroundSharing information derived from individual patient data is restricted by regulatory frameworks due to privacy concerns. Generative artificial intelligence can generate shareable virtual patient populations as proxies for sensitive reference datasets. Explicit demonstration of privacy is demanded.ObjectiveThis study evaluated whether a privacy-by-design technique called “avatars” can generate synthetic datasets replicating all reported information from randomized clinical trials (RCTs).MethodsWe generated 2160 synthetic datasets from two phase 3 RCTs for patients with multiple sclerosis (NCT00213135 and NCT00906399; n=865 and 1516 patients) with different configurations to select one synthetic dataset with optimal privacy and utility for each. Several privacy metrics were computed, including protection against distance-based membership inference attacks. We assessed fidelity by comparing variable distributions and assessed utility by checking that all end points reported in the publications had the same effect directions, were within the reported 95% CIs, and had the same statistical significance.ResultsProtection against membership inference attacks was the hardest privacy metric to optimize, but the technique yielded robust privacy and replication of the primary end points (in 72.5% and 80.8% of the 1080 generated datasets). Utility was uneven across the variables and end points, such that information about some end points could not be captured. With optimized generation configurations, we selected one dataset from each RCT replicating all efficacy end points of the placebo and approved treatment arms while maintaining satisfactory privacy (hidden rate: 85.0% and 93.2%).ConclusionsGenerating synthetic RCT datasets replicating primary and secondary efficacy end points is possible while achieving a satisfactory and explicit level of privacy. To show the potential of this method to unlock health data sharing, we released both placebo arms as open datasets.

As Khan and Brown (1) correctly note, the magnitude of the placebo response in antidepressant trials has increased over the years. But it is not only the placebo response that has increased. So too has the response to antidepressants, a fact that has been widely ignored. In the Walsh et al meta-analysis (2), the correlation between the placebo response and year of publication was r = .45; that between the response to selective serotonin reuptake inhibitors (SSRIs) and year of publication was r = .47, and the difference between the two remained relatively constant. What might account for the finding that the response to both placebo and antidepressants has increased over the years? One thing that it points to is that the drug effect and the placebo effect are probably additive. That is, the response to antidepressants comprises the effect of the drug and the response to placebo, so that when the placebo effect increases, so too does the response to the drug (3). What, then, is responsible for the increase in antidepressant and placebo responses over the years? It cannot be due to decreased baseline severity in more recent trials, because pharmaceutical companies abandoned including mildly and moderately depressed patients in efficacy trials after finding that these patients did not benefit beyond placebo (4). A more likely explanation is that marketing has led to increased public perception that antidepressants are effective, thus enhancing the placebo effect, and because the placebo effect is a component of the drug response, the latter also increased. As Khan and Brown note, “if you lower the risk of exposure to placebo, then the apparent therapeutic effect with the antidepressants and placebo is greater”. Once again, these data suggest additivity. Increasing expectancy of getting a drug rather than a placebo increases the response to the drug and the placebo (5–7). According to Khan and Brown, the last observation carried forward (LOCF) method acts to minimize antidepressant-placebo differences. My colleagues and I made the same assumption when conducting our first meta-analysis of the trial data submitted to the U.S. Food and Drug Administration (FDA) (8). However, the data proved us wrong. LOCF analyses indicated greater drug-placebo differences than did the observed cases method, in which dropouts were excluded from the analyses. Caution is needed when drawing conclusions from data indicating that higher baseline scores are associated with greater improvement in both drug and placebo arms. This is exactly what would be expected based on the statistical artifact of regression toward the mean. It is a very substantial effect that is often ignored. Elsewhere I have shown that when difference scores between two random variables are correlated with the score on one of them, the resulting correlation is about r =. 70 (9). This is the chance standard against which one might judge an association between baseline severity and improvement scores. Finally, a note on the “quick and easy approval” of vilazodone by the FDA is needed. Trovis Pharmaceuticals submitted seven clinical trials to the FDA. The first five showed negative results. However, in one of these, the company noted a non-significant trend toward superiority of vilazodone over placebo on the Montgomery-Asberg Depression Rating Scale (MADRS), but not on the Hamilton Rating Scale for Depression (HAM-D), which had been used in previous antidepressant approvals as the primary outcome for assessing efficacy. Trovis Pharmaceuticals was then allowed to designate the MADRS instead of the HAM-D as the primary outcome measure for what were subsequently considered the two pivotal trials. The difference between vilazodone and placebo on the HAM-D improvement was only 1.69 points on the two “pivotal” trials and, considering all seven trials, it was only 1.01. The FDA approved labeling states that “the efficacy of VIIBRYD was established in two 8-week, randomized, double-blind, placebo-controlled trials”, but makes no mention of the five negative trials. In an internal memo dated May 4, 1998, P. Leber, writing in his capacity as Director of the FDA Division of Neuropharmacological Drug Products, stated his opinion that “labeling that selectively describes positive studies and excludes mention of negative ones can be viewed as being potentially ‘false and misleading’” (10), p. 11). Despite these minor qualifications, the points made by Khan and Brown are well taken. Drug-placebo differences are small in efficacy trials, and most of the response to antidepressants seems due to expectancy.

Genetic variation may differentially modify drug and placebo treatment effects in randomized clinical trials. In asthma, although lung function and asthma control improvements are commonplace with placebo, pharmacogenomics of placebo vs. drug response remains unexamined. In a genomewide association study of subjective and objective outcomes with placebo treatment in Childhood Asthma Management Program of nedocromil/budesonide vs. placebo (N=604), effect estimates for lead single nucleotide polymorphisms (SNPs) were compared across arms. The coughing/wheezing lead SNP, rs2392165 (β=0.94; P=1.10E-07) mapped to BBS9, a gene implicated in lung development that contains a lung function expression quantitative trait locus. The effect was attenuated with budesonide (Pinteraction =1.48E-07), but not nedocromil (Pinteraction =0.06). The lead forced vital capacity SNP, rs12930749 (β=-5.80; P=1.47E-06), mapped to KIAA0556, a locus genomewide associated with respiratory diseases. The rs12930749 effect was attenuated with budesonide (Pinteraction =1.32E-02) and nedocromil (Pinteraction =1.09E-02). Pharmacogenomic analysis revealed differential effects with placebo and drug treatment that could potentially guide precision drug development in asthma.

Problems and challenges in the design of irritable bowel syndrome clinical trials: experience from published trials

Placebo Studies in Developing Countries: Ethical, but not Ideal

BackgroundContextual effects (i.e., placebo response) refer to all health changes resulting from administering an apparently inactive treatment. In a randomized clinical trial (RCT), the overall treatment effect (i.e., the post-treatment effect in the intervention group) can be regarded as the true effect of the intervention plus the impact of contextual effects. This meta-research was conducted to examine the average proportion of the overall treatment effect attributable to contextual effects in RCTs across clinical conditions and treatments and explore whether it varies with trial contextual factors.MethodsData was extracted from trials included in the main meta-analysis from the latest update of the Cochrane review on “Placebo interventions for all clinical conditions” (searched from 1966 to March 2008). Only RCTs reported in English having an experimental intervention group, a placebo comparator group, and a no-treatment control group were eligible.ResultsIn total, 186 trials (16,655 patients) were included. On average, 54% (0.54, 95%CI 0.46 to 0.64) of the overall treatment effect was attributable to contextual effects. The contextual effects were higher for trials with blinded outcome assessor and concealed allocation. The contextual effects appeared to increase proportional to the placebo effect, lower mean age, and proportion of females.ConclusionApproximately half of the overall treatment effect in RCTs seems attributable to contextual effects rather than to the specific effect of treatments. As the study did not include all important contextual factors (e.g., patient-provider interaction), the true proportion of contextual effects could differ from the study’s results. However, contextual effects should be considered when assessing treatment effects in clinical practice.Trial registrationPROSPERO CRD42019130257. Registered on April 19, 2019.

OBJECTIVES/SPECIFIC AIMS: To analyze contemporary study design methods and clinical trial approaches in placebo research. METHODS/STUDY POPULATION: An analysis was conducted on the following studies: I. “Managing” the Placebo Effect: The Single-Blind Placebo Lead-in Response in Two Pain Models by RN Haden, et al. The objective of the study was to consider elements of the placebo response in the context of two pain models using a “single-blind placebo lead-in” design (SBPLI) by engaging the “placebo response” prior to randomization to active drug and placebo-controlled conditions. The methods of the study included two pilot drug trials using knee osteoarthritis (KOA) and non-radicular low back pain (LBP) subjects, SBPLI protocols were conducted. In the first study, 36 subjects with non-radicular CLBP were enrolled in a double-blind, randomized, placebo-controlled trial of hydromorphone ER. In the second study, a total of 42 subjects with chronic KOA pain were enrolled in a double-blind, randomized, placebo-controlled study of milnacipran. Gender and/or diagnosis affected placebo responses as observed in changes in patient self-reported pain, depressive and pain anxiety symptoms were examined. Additionally, the placebo response on performance-based tests (stair climbing, range of motion (ROM), sit to stand repetitions, and 6-minute treadmill distance) was evaluated. II. Randomized Placebo-Controlled Placebo Trial to Determine the Placebo Effect Size by L. Gerdesmeyer, et al. The objective of the study was to analyze the pure placebo effect on clinical, chronic pain through a blinded RCT. The methods of the study included 182 patients suffering from chronic plantar heel pain for over 6 months, who failed to respond to conservative treatments, were screened and 106 of these patients were enrolled into this study. The patients were randomly assigned to receive either a blinded placebo shockwave treatment or an unblinded placebo shockwave treatment. The primary outcome measure was the differences in percentage change of visual analogue scale (VAS) scores 6 weeks after the intervention. The secondary outcome measure was the differences in Roles and Maudsley pain score (RMS) 6 weeks after intervention. III. Open-label placebo treatment in chronic low back pain: a randomized controlled trial by C. Carvalho, et al. The objective of the study was to investigate whether placebo effects in chronic low back pain could be harnessed ethically by adding open-label placebo (OLP) treatment to treatment as usual (TAU) for 3 weeks. The methods of the study included 97 randomized participants in a 3-week randomized control trial comparing current treatment plus OLP to current treatment alone (TAU). RESULTS/ANTICIPATED RESULTS: N/a DISCUSSION/SIGNIFICANCE OF IMPACT: The aforementioned studies provide placebo researchers with contemporary and reliable methodologies to examine placebo effects on participants. These methodologies provide scientists with clinical translational research methodology styles based on the foundation of regulatory science.

Genetics and the placebo effect: the placebome

The placebo effect: From concepts to genes

OP0250 EFFICACY AND SAFETY OF ROMILKIMAB IN DIFFUSE CUTANEOUS SYSTEMIC SCLEROSIS (DCSSC): RANDOMIZED, DOUBLE-BLIND, PLACEBO-CONTROLLED, 24-WEEK, PROOF OF CONCEPT STUDY

Results of the Phase 3 Study of Lenalidomide Versus Placebo As Maintenance Therapy Following Second-Line Treatment for Patients with Chronic Lymphocytic Leukemia (the CONTINUUM Trial)