How randomised controlled trials (RCTs) work.

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.

Bias↗

PubMed2006

Rater bias in a blinded randomized placebo-controlled psychiatry trial.

Rater bias occurs when rater knowledge of treatment assignment modifies the outcome assessment. Raters may be unconsciously influenced by inclinations for or against a particular treatment and consequently may give a more or less generous assessment depending upon these biases. Blinding of raters by keeping raters unaware of treatment assignment is one way to limit bias influencing assessment due to knowledge of treatment assignment. Unblinding may be particularly problematic in efficacy studies comparing placebo to drugs and/or non-drug psychotherapy treatments where subjects may reveal drug side-effects or mention their therapist by name, thus unblinding their treatment assignment. We present a new instrumental variable statistical approach for assessing the association between success in blinding and its impact on efficacy estimates of active drug and/or cognitive behavioural psychotherapy versus placebo in the multicentre comparative treatment study of panic disorder. Despite the uncertainty involved in assessing bias that may be unobserved and unconscious, we will show how to derive a bound for the impact of rater bias.

Bias↗

PubMed2006

Causal conclusions are most sensitive to unobserved binary covariates.

There is a rich literature that considers whether an observed relation between treatment and response is due to an unobserved covariate. In order to quantify this unmeasured bias, an assumption is made about the distribution of this unobserved covariate; typically that it is either binary or at least confined to the unit interval. In this paper, this assumption is relaxed in the context of matched pairs with binary treatment and response. One might think that a long-tailed unobserved covariate could do more damage. Remarkably that is not the case: the most harm is done by a binary covariate, so the case commonly considered in the literature is most conservative. This has two practical consequences: (i) it is always safe to assume that an unobserved covariate is binary, if one is content to make a conservative statement; (ii) when another assumption seems more appropriate, say normal covariate, there will be less sensitivity than with a binary covariate. This assumption implies that it is possible that a relation between treatment and response that is sensitive to unmeasured bias (if the unobserved covariate is dichotomous), ceases to be sensitive if the unobserved covariate is normally distributed. These ideas are illustrated by three examples. It is important to note that the claim in this paper applies to our specific setting of matched pairs with binary treatment and response. Whether the same conclusion holds in other settings is an open question.

Bias↗