PubMed · 7651565
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A W Hoes. 1995. Case-control studies.. https://doi.org/10.1016/0300-2977(95)00031-h
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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.
Estimates of prevalence in epidemiologic surveys are prone to bias due to selective response. Therefore, much effort is devoted to reduce the number of nonrespondents. For example, individuals who do not respond in the first round of recruitment in mail surveys are usually contacted a second (or even third or fourth) time yielding consecutive waves of responses. Yet this sequence of waves is often neglected in epidemiologic analyses in that prevalence is simply estimated as the proportion of trait-positive individuals among the total group of respondents. This paper investigates alternative estimates of prevalence that might be used in surveys with two waves of respondents. The estimates are based on different assumptions on the relation of response rates with the trait of interest. As this relation is likely to vary from survey to survey depending on the specific circumstances under which the recruitment of participants is conducted, none of the estimates is universally preferable. The performance of the different estimates is assessed in a variety of hypothetical and empirical examples, and strategies are discussed to make the best use of the different estimates in the analysis of epidemiologic studies.
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