DOI: 10.25934/pr00008937
title: Predictors of placebo response in opioid trials for patients with Chronic Primary Pain – An individual-patient data meta-analysis 

description: Acronyms: 
CPP – Chronic Primary Pain – Chronic Pain in one or more bodily regions, persists or recurs for more than three months and is associated with significant emotional distress and functional disability
IPD – Individual Patient Data – Study data from individual patients participating in the respective study 
RCT – Randomized Controlled Trial – Most common trial design 

Many studies in patients with chronic pain found that the effect of opioids was only moderate compared to placebo. At the same time, many patients show a relevant placebo response in clinical trials, and report significant pain relief. The placebo response is especially high in trials that examine an opioid, probably due to higher expectations regarding pain relief that patients have when entering such a trial. In our analysis, we use individual patient data from several studies to look at patient and trial differences that might help explain who experiences a high placebo response - and who does not. Further, we are also interested in the question whether the number of study arms influence the response of patients to both opioid and placebo. Therefore, we use data from studies that have already been conducted, and look at invididual differences rather than mean differences (of samples). This will allow us to gain more detailed knowledge about who is more likely to exhibit a high placebo response - and under which conditions. 


Necessity of the research: The findings of this study will have implications for the interpretation and design of placebo-controlled clinical trials in the field of opioids. Detailed information about the placebo response and its determinants helps to interpret opioid-placebo differences (i.e., ‘assay sensitivity’) and, in turn, prevents an under- or overestimation of the specific opioid effect in comparison with nonspecific effects. This knowledge will also be of interest for clinicians and patients, enabling to design an evidence-based treatment plan. 

How many patients are potentially affected: Patients already participated in the studies we aim to analyze. Thus, we will re-examine data that has been already collected. No additional effort is needed from patients’ side. However, the gained findings will be of general interest for patients with CPP being treated with opioids. 

How the research will add to medical science or patient care: Future RCTs could try to minimize placebo responses and thereby limit their influence on the overall findings with different strategies: (a) to statistically control for patient characteristics shown to be related to placebo responses; and (b) to adapt the study design by considering trial characteristics associated with placebo responses. Furthermore, the findings will have implications for the clinical practice. There, the gained knowledge will allow to maximize and personalize placebo responses within an ethical framework (e.g., to foster a patient-physician relationship that is based on trust). 

How the research will be conducted: We aim to conduct an individual patient data (IPD) meta-analysis. A meta-analysis combines various trials that have been already conducted, allowing to calculate an overall effect size. In a first step, the original trial data will be sought from the sponsors and / or authors of all studies fulfilling the inclusion criteria. In a second step, we will combine the study data into one master file. Then, we will calculate the placebo effect size and its determinants. 

What design and methods you have chosen and why: We did choose to use an IPD. Data sharing agreements make it possible for researchers to apply IPD instead of aggregate data. IPD results in more clinically relevant results than a meta-analysis of aggregate data, going beyond the “grand mean” toward individualized medicine. Thus, the estimation of results for each covariate value allows different recommendations to be made for different subgroups of patients; personalising treatment in this way can benefit patients and ensure the cost‐effective use of health.