We propose to conduct a systematic review and meta-analysis of randomized controlled trials to summarize the evidence for use of opioids to treat chronic non-cancer pain.
We will identify relevant randomized controlled trials, in any language, by a systematic search of CINAHL, EMBASE, MEDLINE, AMED, HealthSTAR, PsycINFO, and the Cochrane Central Registry of Controlled Trials, from inception of the databases, with terms designed by a highly experienced medical librarian to capture studies enrolling patients with any form of chronic non-cancer pain managed with opioids. Our librarian has refined our search strategy for each individual database. Reviewers will scan the bibliographies of all retrieved trials and other relevant publications, including reviews and meta-analyses, for additional relevant articles.
Types of study to be included
Randomized controlled trials.
Condition or domain being studied
Chronic non-cancer pain (CNCP) comprises any painful condition that persists for >=3 months and is not associated with neoplastic disease.
Patients presenting with any form of chronic non-cancer pain.
Any type of opioid analgesic.
Any type of non-opioid control.
2. Physical functioning (including Quality of Life)
3. Emotional functioning
4. Participant rating of improvement and satisfaction with treatment
5. Adverse symptoms and adverse events
6. Participant disposition (e.g. adherence to the treatment regime and reasons for premature withdrawal from the trial)
7. Role functioning (i.e. work and educational activities, social and recreational activities, home and family care)
8. Interpersonal functioning (i.e. interpersonal relationships, sexual activities)
9. Sleep & Fatigue
Data extraction, (selection and coding)
Using a standardized form, reviewers trained in health research methodology will work in pairs to screen, independently and in duplicate, titles and abstracts of identified citations and acquire the full text publication of any article that either reviewer judges as potentially eligible. Using a standardized form the same reviewer teams will independently apply eligibility criteria to the full text of potentially eligible trials. Eligible trials will meet the following criteria: (1) random allocation of patients to an opioid analgesic or a non-opioid control; (2) inclusion of patients presenting with CNCP. We will contact study authors if limitations in reporting lead to uncertainties in eligibility, risk of bias, or outcome.
Risk of bias (quality) assessment
Reviewers will assess risk of bias using a modified Cochrane risk of bias instrument that include response options of “definitely or probably yes” – assigned a high risk of bias - or “definitely or probably no” - assigned a low risk of bias, an approach we have previously shown to be valid. We will evaluate allocation concealment; blinding of participants, clinicians, data collectors, outcome assessors, and data analysts; incomplete outcome data; and selective outcome reporting. Reviewers will resolve disagreement by discussion.
Strategy for data synthesis
We will use a number of approaches to provide interpretable results from our meta-analyses. For studies that provide binary outcome measures, we will calculate the risk difference (RD), relative risk (RR), number needed to treat (NNT) and odds ratio (OR).
When pooling across studies reporting continuous endpoints that use the same instrument, we will calculate the weighted mean difference (WMD) which maintains the original unit of measurement and represents the average difference between groups. The underlying principle of ‘weighting’ by inverse of variance is to accord more weight to studies that provide more information about the treatment effect. Once the WMD has been calculated, we will contextualize this value through the minimally important difference (MID) - the smallest change in instrument score that patients perceive is important. Establishing the MID requires comparison with an independent standard or anchor that is itself interpretable, and to which the instrument under investigation is at least moderately correlated. If an anchor-based MID has not been established for a continuous outcome measure used to assess treatment effect on CNCP we will assume that ½ standard deviation on the instrument score represents the MID.
This presentation does not deal with the risk that clinicians may interpret all mean effects below the MID as unimportant, and presume important benefit for all patients when the mean difference exceeds the MID. We will address this issue by applying the MID to individual studies, estimate proportions in these studies, and then aggregate the results in order to provide a summary estimate of the proportion of patients who benefit from treatment.
For trials that use different continuous outcome measures that address the same underlying domain, we will design our analysis based on the recommendations by Thorlund et al. Specifically, we will calculate the between-group difference in change scores (change from baseline) and divide this difference by the standard deviation (SD) of the change. This calculation creates a unitless measure of the effect (quantifying its magnitude in number of standard deviations) called the standardized mean difference (SMD) that allows for comparison and pooling across trials. If the MID is established for all instruments we will use this measure to convert the summary effect into a RR, RD, OR, and NNT. We will complement this presentation by either converting the summary effect into natural units of a widely accepted instrument used to measure changes in the domain of interest or, if such an instrument is not available, we will substitute the MID for the SD (denominator) in the SMD equation which will result in more-readily interpretable MID units instead of SD units. MID units are also not vulnerable to the distortions that varying heterogeneity of populations can create.
For continuous outcomes in which an anchor-based MID has not been established we will assume a meta-analysis control group probability (pC) and use the SMD to calculate the RD, OR, and NNT.
We will use random effects meta-analyses, which are conservative in that they consider both within and among study differences in calculating the error term used in the analysis. We will examine heterogeneity using both a chi-squared test and the I-squared statistic, the percentage of variability that is due to true differences between studies (heterogeneity) rather than sampling error (chance).
Analysis of subgroups or subsets
We have generated the following a priori hypotheses to explain variability between studies: (1) subjective syndromes (e.g. fibromyalgia) will show less effect vs. objectively diagnosed conditions (e.g. rheumatoid arthritis); (2) trials comparing opioids to placebo will show larger effects than trials using active comparators; (3) patients receiving disability benefits or involved in litigation will show less effect vs. those that are not; (4) weaker opioids will show less of a treatment effect than stronger opioids; and (5) trials with higher risk of bias will show larger effects than trials with lower risk of bias. We will conduct tests of interaction to establish if subgroups differ significantly from each other and use the GRADE criteria to evaluate the quality of evidence by outcome.
Contact details for further information
Jason W. Busse, DC, PhD
Assistant Professor, Departments of Anesthesia and Clinical Epidemiology & Biostatistics
McMaster University, HSC-2U1
1280 Main St. West,
Hamilton, Ontario, L8S 4K1
Organisational affiliation of the review
Dr Jason Busse, McMaster University Professor Norman Buckley, McMaster University Mr Shanil Ebrahim, McMaster University Professor Gordon Guyatt, McMaster University Professor Daniel Sessler, Cleveland Clinic Lerner Research Institute Dr Bradley Johnston, Hospital for Sick Children Dr John Riva, McMaster University Dr Stefan Schandelmaier, Asim Lehre und Forschung Universitatsspital Basel Professor Regina Kunz, asim Academy of Swiss Insurance Medicine
Ms Iris Krawchenko, Dell Pharmacies Ltd. Ms Marg Bellman, Sun Life FInancial Dr Maria Calvo, McMaster University Dr Ainlsey Moore, McMaster University
Anticipated or actual start date
01 March 2012
Anticipated completion date
01 August 2013
Canadian Institutes of Health Research
Conflicts of interest
Canada, Switzerland, United States of America
Subject index terms status
Subject indexing assigned by CRD
Subject index terms
Analgesics, Opioid; Chronic Pain; Humans
Date of registration in PROSPERO
28 September 2012
Date of publication of this revision
28 September 2012
Stage of review at time of this submission
Piloting of the study selection process
Formal screening of search results against eligibility criteria
Risk of bias (quality) assessment
PROSPERO This information has been provided by the named contact for this review. CRD has accepted this information in good faith and registered the review in PROSPERO. CRD bears no responsibility or liability for the content of this registration record, any associated files or external websites.