The role of quality of life data as an endpoint for collecting real-world evidence within geroscience clinical trials

With geroscience research evolving at a fast pace, the need arises for human randomized controlled trials to assess the efficacy of geroprotective interventions to prevent age-related adverse outcomes, disease, and mortality in normative aging cohorts. However, to confirm efficacy requires a long-term and costly approach as time to the event of morbidity and mortality can be decades. While this could be circumvented using sensitive biomarkers of aging, current molecular, physiological, and digital endpoints require further validation. In this review, we discuss how collecting real-world evidence (RWE) by obtaining health data that is amenable for collection from large heterogeneous populations in a real-world setting can help speed up validation of geroprotective interventions. Further, we propose inclusion of quality of life (QoL) data as a biomarker of aging and candidate endpoint for geroscience clinical trials to aid in distinguishing healthy from unhealthy aging. We highlight how QoL assays can aid in accelerating data collection in studies gathering RWE on the geroprotective effects of repurposed drugs to support utilization within healthy longevity medicine. Finally, we summarize key metrics to consider when implementing QoL assays in studies, and present the short-form 36 (SF-36) as the most well-suited candidate endpoint.

When does the placebo effect have an impact on network meta-analysis results?

The placebo effect is the 'effect of the simulation of treatment that occurs due to a participant's belief or expectation that a treatment is effective'. Although the effect might be of little importance for some conditions, it can have a great role in others, mostly when the evaluated symptoms are subjective. Several characteristics that include informed consent, number of arms in a study, the occurrence of adverse events and quality of blinding may influence response to placebo and possibly bias the results of randomised controlled trials. Such a bias is inherited in systematic reviews of evidence and their quantitative components, pairwise meta-analysis (when two treatments are compared) and network meta-analysis (when more than two treatments are compared). In this paper, we aim to provide red flags as to when a placebo effect is likely to bias pairwise and network meta-analysis treatment effects. The classic paradigm has been that placebo-controlled randomised trials are focused on estimating the treatment effect. However, the magnitude of placebo effect itself may also in some instances be of interest and has also lately received attention. We use component network meta-analysis to estimate placebo effects. We apply these methods to a published network meta-analysis, examining the relative effectiveness of four psychotherapies and four control treatments for depression in 123 studies.

Contextual effects: how to, and how not to, quantify them

The importance of contextual effects and their roles in clinical care controversial. A Cochrane review published in 2010 concluded that placebo interventions lack important clinical effects overall, but that placebo interventions can influence patient-reported outcomes such as pain and nausea. However, systematic reviews published after 2010 estimated greater contextual effects than the Cochrane review, which stems from the inappropriate methods employed to quantify contextual effects. The effects of medical interventions (i.e., the total treatment effect) can be divided into three components: specific, contextual, and non-specific. We propose that the most effective method for quantifying the magnitude of contextual effects is to calculate the difference in outcome measures between a group treated with placebo and a non-treated control group. Here, we show that other methods, such as solely using the placebo control arm or calculation of a 'proportional contextual effect,' are limited and should not be applied. The aim of this study is to provide clear guidance on best practices for estimating contextual effects in clinical research.

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.

Placebo response in trials of drug treatments for cancer-related fatigue: a systematic review, meta-analysis and meta-regression

BACKGROUND

Cancer-related fatigue (CRF) is one of the most distressing symptoms experienced by patients. There is no gold standard treatment, although multiple drugs have been tested with little evidence of efficacy. Randomised controlled trials (RCTs) of these drugs have commented on the existence or size of the placebo response (PR). The objective of this systematic review was to establish the magnitude of the PR in RCTs of drugs to relieve CRF and to identify contributing factors.

METHOD

RCTs were included in which the objective was to treat CRF. A meta-analysis was conducted using the standardised mean change (SMC) between baseline and final measurement in the placebo group. To explore factors that may be associated with the PR (eg, population or drug), a meta-regression was undertaken. Risk of bias was assessed using the revised Cochrane tool.

RESULTS

From 3916 citations, 30 relevant RCTs were identified. All had limitations that increased their risk of bias. The pooled SMC in reduction in fatigue status in placebo groups was -0.23 (95% confidence intervals -0.42 to -0.04). None of the variables analysed in the meta-regression were statistically significant related to PR.

CONCLUSION

There is some evidence, based on trials with small samples, that the PR in trials testing drugs for CRF is non-trivial in size and statistically significant. We recommend that researchers planning drug studies in CRF should consider implementing alternative trial designs to better account for PR and decrease impact on the study results.