PubMed · 14770385
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
Pamela E Windle. 2004. Delphi technique: assessing component needs.. https://doi.org/10.1016/j.jopan.2003.11.005
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
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.
Consider a case-control study designed to investigate the possible association between development of a particular disease and the value of a putative risk factor measured on an ordinal scale. Let E denote a subject's true risk factor value and let E* denote a subject's recorded risk factor value. Misclassification bias occurs if conclusions reached regarding the relationship between disease status and E* do not also apply to the relationship between disease status and E. We propose a model for the conditional probability distribution of E* given E. We show how the model may be used to investigate misclassification bias in a validation study where measurements of E* and E are available for both cases and controls and apply the methods developed to data from a test-retest study of recall bias in the context of screening for hypertension. We also consider a situation where the validation study is carried out on a subset of the subjects within a larger case-control study. In that case, values for E* are available for all subjects but values for E are available only for those subjects included in the validation study. We show how correct likelihood-based inference concerning association between disease status and risk factor value may be carried out using all of the available data. A Monte Carlo study shows how the inclusion of a validation study leads to a correction of recall bias problems at the cost of an increased standard error for the estimated association parameter.
In some clinical trials, treatment allocation on a patient level is not feasible, and whole groups or clusters of patients are allocated to the same treatment. If, for example, a clinical trial is investigating the efficacy of various patient coaching methods and randomization is done on a patient level, then patients who are receiving different methods may come into contact with each other and influence each other. This would create contamination of the treatment effects. Such bias might be prevented by randomization on the coaches level. The patients of a coach constitute a cluster and all the subjects in that cluster receive the same treatment. Disadvantages of this approach may be reduced statistical efficiency and recruitment bias, as the treatment that a subject will receive is known in advance. Pseudo cluster randomization avoids this, because in pseudo cluster randomization, not everybody in a certain cluster receives the same treatment, just the majority. There are two groups of clusters: in one group the majority of subjects receive treatment A, while a limited number receive treatment B. In the other group of clusters the proportions are reversed. The statistical properties of this method are described. When contamination is present, the method appears to be more efficient than randomization on a patient level or on a cluster level.