PubMed · 9921601
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M Livingston. 1999-01-01. Quebec Task Force's whiplash study.. https://doi.org/10.1097/00007632-199901010-00025
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In randomized controlled trials (RCTs), study participants and research personnel are often blinded to minimize biases related to knowing treatment allocation. To determine if blinding was effective, participants may be asked which treatment they believe they received ("treatment guess"). This descriptive review characterized blinding assessment (BA) reporting in pharmacotherapy and neuromodulation neuropathic pain RCTs. Of 288 papers, 36 (12.5%) reported a BA. One paper reported the results of 2 studies, so in total 37 studies with a BA were assessed. Of these, 19 were crossover, 17 parallel, and 1 partial crossover in design. All 37 studies assessed participant blinding, and 10 also assessed investigator blinding. Approximately 27% included an "unsure" answer option for treatment guess, and 38% asked the reason for the guess. There were no clear patterns in BA reporting across time nor based on treatment type. Seventeen trials provided sufficient data to calculate Bang Blinding Index (BI) to determine blinding success. Participants remained blinded (BI = 0 ± 0.2) in 10/17 placebo and 10/17 treatment arms, 6 placebo and 5 treatment arms had a BI > 0.2 suggesting possible unblinding, whereas 1 placebo and 2 treatment arms had a BI < -0.2 suggesting misinformed guessing. Overall, we found that BAs are done in a minority of published neuropathic pain trials and with variable methodology. Given the importance of minimizing risk of bias because of treatment unblinding, future studies should consider including BAs, and further consensus building is necessary to determine if and how BAs should be conducted and interpreted in analgesic clinical trials.
Despite long-standing critiques of the conduct of underpowered clinical trials, the practice not only remains widespread, but also has garnered increasing support. Patients and healthy volunteers continue to participate in research that may be of limited clinical value, and authors recently have offered 2 related arguments to support the validity and value of underpowered clinical trials: that meta-analysis may "save" small studies by providing a means to combine the results with those of other similar studies to enable estimates of an intervention's efficacy, and that although small studies may not provide a good basis for testing hypotheses, they may provide valuable estimates of treatment effects using confidence intervals. In this article, we examine these arguments in light of the distinctive moral issues associated with the conduct of underpowered trials, the disclosures that are owed to potential participants in underpowered trials so they may make autonomous enrollment decisions, and the circumstances in which the prospects for future meta-analyses may justify individually underpowered trials. We conclude that underpowered trials are ethical in only 2 situations: small trials of interventions for rare diseases in which investigators document explicit plans for including their results with those of similar trials in a prospective meta-analysis, and early-phase trials in the development of drugs or devices, provided they are adequately powered for defined purposes other than randomized treatment comparisons. In both cases, investigators must inform prospective subjects that their participation may only indirectly contribute to future health care benefits.
Biases in systematic reviews and meta-analyses may be examined in 'meta-epidemiological' studies, in which the influence of trial characteristics such as measures of study quality on treatment effect estimates is explored. Published studies to date have analysed data from collections of meta-analyses with binary outcomes, using logistic regression models that assume that there is no between- or within-meta-analysis heterogeneity. Using data from a study of publication bias (39 meta-analyses, 394 published and 88 unpublished trials) and language bias (29 meta-analyses, 297 English language trials and 52 non-English language trials), we compare results from logistic regression models, with and without robust standard errors to allow for clustering on meta-analysis, with results using a 'meta-meta-analytic' approach that can allow for between- and within-meta-analysis heterogeneity. We also consider how to allow for the confounding effects of different trial characteristics. We show that both within- and between meta-analysis heterogeneity may be of importance in the analysis of meta-epidemiological studies, and that confounding exists between the effects of publication status and trial quality.