Randomized treatment-belief trials

Joint evaluation of placebo and treatment effects in cluster randomized trials by causal inference models

The term placebo effect refers to the psychobiological effect of a patient's knowledge or belief of being treated. A placebo effect is patient-driven, which makes it fundamentally different from the usual treatment effect resulting from external actions. In modern clinical research, the presence of a placebo effect is often treated as a nuisance issue, something to be "adjusted away" in estimating a treatment effect of primary interest. However, from a patient-centered perspective, we believe that a possible placebo produces substantial improvements in patient-centered outcomes. Understanding placebo effects is therefore an important part of patient-centered outcomes research. The available methods for estimating placebo effects are designed for individually randomized trials and are not directly applicable to cluster randomized trials (CRTs). There are several challenges in estimating placebo effects in CRTs. A major challenge is the possible presence of interference within clusters, in the sense that a subject's outcome may depend on the beliefs subjects in the same cluster about treatment assignment (mentality) and therefore possible correlation in outcome and mentality among subjects exists in the same cluster. In this article, we extend the previously developed causal inference framework to also encompass CRTs, using the G-Computation and inverse probability weighting (IPW) approaches. We also develop methodologies and further extend the G-Computation and IPW approaches to handle missingness for jointly evaluating placebo effect and treatment-specific effect, specifically in the context of CRTs. The proposed methods are demonstrated in simulation studies and a cluster randomized trial on effect of fermented dairy drink.

Statistical methods in handling placebo effect

A critical issue facing the therapeutic area of neurological diseases is the large number of failed randomized clinical trials, especially when moving from promising Phase 2 trials to failed Phase 3 trials. A common cited reason for these failures is a high placebo response rate that thereby reduces the observed treatment effect. Explanations for this higher than anticipated placebo response include small sample sizes, inadequate study designs and/or analytic methods, baseline characteristics of the trial sample, possible investigator bias and a participant's own expectations and conditional learning. Several innovative study designs and new methodological approaches to statistical analyses have been proposed to handle placebo effects anticipated or observed in double blind, randomized clinical trials (RCT's). This chapter examines current study designs being used to reduce the observed placebo response and statistical analysis methods being employed for addressing this problem in neuroscience clinical trials.

Sample size calculations for blinding assessment

Blinding is a critical component in randomized clinical trials along with treatment effect estimation and comparisons between the treatments. Various methods have been proposed for the statistical analyses of blinding-related data, but there is little guidance for determining the sample size for this type of data, especially if blinding assessment is done in pilot studies. In this paper, we try to fill this gap and provide simple methods to address sample size calculations for a "new" study with different research questions and scenarios. The proposed methods are framed in terms of estimation/precision or statistical testing to allow investigators to choose the best suited method for their goals. We illustrate the methods using worked examples with real data.

Exploring the Effectiveness of External Use of Bach Flower Remedies on Carpal Tunnel Syndrome: A Pilot Study

BACKGROUND

A randomized, pilot, placebo-controlled clinical trial was conducted with the aim of evaluating the effectiveness of a cream based on Bach flower remedies (BFR) on symptoms and signs of carpal tunnel syndrome.

METHODS

Forty-three patients with mild to moderate carpal tunnel syndrome during their "waiting" time for surgical option were randomized into 3 parallel groups: Placebo (n = 14), blinded BFR (n = 16), and nonblinded BFR (n = 13). These groups were treated during 21 days with topical placebo or a cream based on BFR.

RESULTS

Significant improvements were observed on self-reported symptom severity and pain intensity favorable to BFR groups with large effect sizes (η2partial > 0.40). In addition, all signs observed during the clinical exam showed significant improvements among the groups as well as symptoms of pain, night pain, and tingling, also with large effect sizes (φ > 0.5). Finally, there were significant differences between the blinded and nonblinded BFR groups for signs and pain registered in clinical exam but not in self-reports.

CONCLUSION

The proposed BFR cream could be an effective intervention in the management of mild and moderate carpal tunnel syndrome, reducing the severity symptoms and providing pain relief.

Using instrumental variables to disentangle treatment and placebo effects in blinded and unblinded randomized clinical trials influenced by unmeasured confounders

Clinical trials traditionally employ blinding as a design mechanism to reduce the influence of placebo effects. In practice, however, it can be difficult or impossible to blind study participants and unblinded trials are common in medical research. Here we show how instrumental variables can be used to quantify and disentangle treatment and placebo effects in randomized clinical trials comparing control and active treatments in the presence of confounders. The key idea is to use randomization to separately manipulate treatment assignment and psychological encouragement conversations/interactions that increase the participants' desire for improved symptoms. The proposed approach is able to improve the estimation of treatment effects in blinded studies and, most importantly, opens the doors to account for placebo effects in unblinded trials.

Random Guess and Wishful Thinking are the Best Blinding Scenarios

Blinding is a methodologic safeguard of treatment evaluation, yet severely understudied empirically. Mathieu et al.'s theoretical analysis (2014) provided an important message that blinding cannot eliminate potential for bias associated with belief about allocation in randomized controlled trial; just like the intent-to-treat principle does not guarantee unbiased estimation under noncompliance, the blinded randomized trial as a golden standard may produce bias. They showed possible biases but did not assess how large the bias could be in different scenarios. In this paper, we examined their findings, and numerically assessed and compared the bias in treatment effect parameters by simulation under frequently encountered blinding scenarios, aiming to identify the most ideal blinding scenarios in practice. We conclude that Random Guess and Wishful Thinking (e.g., participants tend to believe they received treatment) are the most ideal blinding scenarios, incurring minimal bias. We also find some evidence that imperfect or partial blinding can be better than no blinding.

Joint Estimation of Treatment and Placebo Effects in Clinical Trials with Longitudinal Blinding Assessments

In some therapeutic areas, treatment evaluation is frequently complicated by a possible placebo effect (i.e., the psychobiological effect of a patient's knowledge or belief of being treated). When a substantial placebo effect is likely to exist, it is important to distinguish the treatment and placebo effects in quantifying the clinical benefit of a new treatment. These causal effects can be formally defined in a joint causal model that includes treatment (e.g., new versus placebo) and treatmentality (i.e., a patient's belief or mentality about which treatment she or he has received) as separate exposures. Information about the treatmentality exposure can be obtained from blinding assessments, which are increasingly common in clinical trials where blinding success is in question. Assuming that treatmentality has a lagged effect and is measured at multiple time points, this article is concerned with joint evaluation of treatment and placebo effects in clinical trials with longitudinal follow-up, possibly with monotone missing data. We describe and discuss several methods adapted from the longitudinal causal inference literature, apply them to a weight loss study, and compare them in simulation experiments that mimic the weight loss study.

Assessing blinding in trials of psychiatric disorders: a meta-analysis based on blinding index

The assessment of blinding in RCTs is rarely performed. Currently most studies that do report data on evaluation of blinding merely report percentages of correct guessing, not taking into account correct guessing by chance. Blinding assessment using the blinding index (BI) has never been performed in a systematic review on studies of major psychiatric disorders. This study is a systematic review of psychiatric randomized control trials using the BI as a chance-corrected measurement of blinding, a tool to analyze and understand the patterns of blinding across studies of major psychiatric disorders with available data. Of 2467 psychiatric RCTs from 2000 to 2010, 66 reported on blinding and 40 studies were found to have enough information on evaluation of blinding to be analyzed using the BI. The experimental treatment groups had an average BI value of 0.14 and the control groups had an average BI value of 0.00. The most common BI scenario was random-random, indicating ideal blinding. A positive correlation between effect size and more correct guesses was also found. Overall, based on BI values and the most common blinding scenario, the published articles on major psychiatric disorders from 2000 to 2010, which reported on blinding assessment for patients, were effectively blinded.

The questionable use of unequal allocation in confirmatory trials

Randomization is the standard means for addressing known and unknown confounders within the patient population in clinical trials. Although random assignment to treatment arms on a 1:1 basis has long been the norm, many 2-armed confirmatory trials now use unequal allocation schemes where the number of patients receiving investigational interventions exceeds those in the comparator arm. In what follows, we offer 3 arguments for why investigators, institutional review boards, and data and safety monitoring boards should exercise caution when planning or reviewing 2-armed confirmatory trials involving unequal allocation ratios. We close by laying out some of the conditions where uneven allocation can be justified ethically.