PubMed · 12529605
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Alison Abbott. 2003-01-16. Ethics panel attacks environment book.. https://doi.org/10.1038/421201a
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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.
A basic estimation strategy in sample surveys is to weight units inversely proportional to the probability of selection and response. Response weights in this method are usually estimated by the inverse of the sample-weighted response rate in an adjustment cell, that is, the ratio of the sum of the sampling weights of respondents in a cell to the sum of the sampling weights for respondents and non-respondents in that cell. We show by simulations that weighting the response rates by the sampling weights to adjust for design variables is either incorrect or unnecessary. It is incorrect, in the sense of yielding biased estimates of population quantities, if the design variables are related to survey non-response; it is unnecessary if the design variables are unrelated to survey non-response. The correct approach is to model non-response as a function of the adjustment cell and design variables, and to estimate the response weight as the inverse of the estimated response probability from this model. This approach can be implemented by creating adjustment cells that include design variables in the cross-classification, if the number of cells created in this way is not too large. Otherwise, response propensity weighting can be applied.
Telephone surveys are widely used in the U.S.A. for the study of health-related topics. They are subject to 'coverage bias' because they cannot sample households that do not have telephones. Although only around 5 per cent of households do not have a telephone, rates of telephone coverage show substantial variation by geography, demographic factors and socio-economic factors. In particular, lack of telephone service is more common among households that contain ethnic and racial minorities or that have lower socio-economic status with fewer opportunities for access to medical care and poorer health outcomes. Thus, failure to adequately account for households without telephones in health surveys may yield estimates of health outcomes that are misleading, particularly in states with at least moderate telephone non-coverage. The dynamic nature of the population of households without telephones offers a way of accounting for such households in telephone surveys. At any given time the population of telephone households includes households that have had a break or interruption in telephone service. Empirical results strongly suggest that these households are very similar to households that have never had telephone service. Thus, sampled households that report having had an interruption in telephone service may be used also to represent the portion of the population that has never had telephone service. This strategy can lead to a reduction in non-coverage bias in random-digit-dialling surveys. This paper presents two methods of adjusting for non-coverage of non-telephone households. The effectiveness of these methods is examined using data from the National Health Interview Survey. The interruption-in-telephone-service methods reduce non-coverage bias and can also result in a lower mean squared error. The application of the interruption-in-telephone-service methods to the National Immunization Survey is also discussed. This survey produces estimates for the 50 states and 28 urban areas. The interruption-in-telephone-service estimates tend be slightly lower than estimates resulting from poststratification and from another non-coverage adjustment method. The results suggest that the reduction in bias is greatest for variables that are highly correlated with the presence or absence of telephone service.