PubMed · 12896891
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David Jarjoura. 2003. Inferences from aggregated data.. https://doi.org/10.1111/j.1553-2712.2003.tb00633.x
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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.
This paper describes a new method for estimating the size of an illicit-drug-using population. It is designed to overcome certain limitations of registry-based techniques that require both comprehensive site coverage and unique case identifiers, and which do not typically provide estimates of the number of drug users who are currently active. The approach involves collecting retrospective self-report data on the careers of individuals who appear at drug treatment programmes. A model is developed that corrects for the selection bias introduced by the sampling plan, and which allows us to estimate the rate at which drug users generate treatment admission events during spells of use. The size of the drug-using population is estimated by dividing the estimated total number of treatment admissions that are generated during some fixed interval of time by the estimated rate at which individuals generate such events. The technique is tested in a series of simulation studies which demonstrate that accurate estimates of the size of the drug using population can be obtained in this manner. Analytical expressions for confidence intervals about the population estimates are derived as part of the exercise. Limitations of the approach and other potential applications are discussed.
Non-differential measurement error in the exposure variable is known to attenuate the dose-response relationship. The amount of attenuation introduced in a given situation is not only a function of the precision of the exposure measurement but also depends on the conditional variance of the true exposure given the other independent variables. In addition, confounder effects may also be affected by the exposure measurement error. These difficulties in statistical model development are illustrated by examples from a epidemiological study performed in the Faroe Islands to investigate the adverse health effects of prenatal mercury exposure.