PubMed · 8096302
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P Easterbrook, J Berlin. 1993-04-10. Meta-analysis.. https://pubmed.ncbi.nlm.nih.gov/8096302/
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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.
As the incidence of tuberculosis (TB) has increased in the United States, occupationally acquired TB has increased among the health care workers (HCWs). This paper describes a model developed in response to the needs of an outbreak of multidrug-resistant TB. One of the goals of the outbreak investigation was to estimate the risk of tuberculin skin test (TST) conversion as a function of HCW job type and the period during which persons were employed over the study period. TST conversions were evaluated at periodic examinations and data are interval-censored. We present a generalized linear model that extends Efron's survival model for censored survival data to the case of interval-censored data.
The aim of this paper is to provide accurate estimation methods for regression models used in epidemiological time series to deduce quantitative morbidity relationships. Such models often include highly correlated variables (pollutant levels and climatic conditions) as well as lagged and unlagged values of the same variables (which also show a high collinearity due to the stochastic dependency of consecutive measurements). We first describe some methods to detect and assess multicollinearity. We recall the drawbacks of usual methods of estimation, and then after briefly mentioning traditional solutions, we explore three alternative methods accounting for multicollinearity: Sclove's estimation; Almon's method; and a combination of Almon's method and principal components procedure. We compare these methods in obtaining efficient estimators on environmental epidemiological data (children's hospital admissions as dependent variable and unlagged and lagged values of outdoor temperature, SO2, NO and CO as explanatory variables.