Single or double data entry: considerations based on a simple binomial model.

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.

Bias↗

PubMed1999

Clinical trials using HIV-1 RNA-based primary endpoints: statistical analysis and potential biases.

Clinical trial endpoints based on magnitude of reduction in HIV-1 RNA levels provide an important complement to endpoints based on percentage of patients achieving complete virologic suppression. However, interpretation of magnitude of reduction can be biased by measurement limitations of virologic assays, particularly lower and upper limits of quantification. Using data from two AIDS Clinical Trials Group (ACTG) studies, widely used crude methods of analyzing HIV-1 RNA reductions were compared with methods that take into account censoring of HIV-1 RNA measurements. Such methods include Kaplan-Meier and censored regression analyses. It was found that standard crude methods of analysis consistently underestimated treatment effects. In some cases, the bias induced by crude methods masked statistically significant differences between treatment arms. Although statistically significant, adjustment for baseline HIV-1 RNA levels had little effect on estimated treatment differences. Furthermore, convenient parametric analyses performed as well as more complex nonparametric analyses. It is concluded that conveniently implemented censored data analyses should be conducted in preference to widely used crude analyses of magnitude of HIV-1 RNA reduction. To obtain complete information about virologic response to antiretroviral therapy, such analyses of magnitude of virologic response should be used to complement analyses of the percentage of patients having complete virologic suppression.

Bias↗

PubMed1999

Potential for bias in estimating human fecundability parameters: a comparison of statistical models.

Fecundability studies, where couples attempting pregnancy subject to 'failure' (conception) one time in each menstrual cycle, present a natural discrete failure-time scenario. Because the biologic capacity to conceive varies among couples in the population, a complication arises in choosing a method of analysis, related to the fact that the maximum follow-up time can vary from study to study, and follow-up time could potentially have different effects on parameters based on different approaches to modelling. Traditional development in fertility studies has been based on an implicit assumption that binary outcomes for different menstrual cycles are mutually independent. We contrast traditional models to a random effects model where cycle viability is modelled as subject-specific. We clarify the interpretations for different parameters from different models. We show that the traditional approach yields some regression parameters that depend on follow-up time, limiting the generalizability of inferences based on this analytic approach. By contrast, the subject-specific model consistently estimates parameters of interest, if the underlying distribution is properly specified. Data from a fecundability study carried out in North Carolina serves to illustrate these points.

Bias↗