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↗

PubMed2000

Gigerenzer's normative critique of Kahneman and Tversky.

Gigerenzer has argued that it may be inappropriate to characterize some of the biases identified by Kahneman and Tversky as "errors" or "fallacies," for three reasons: (a) according to frequentists, no norms are appropriate for single-case judgments because single-case probabilities are meaningless; (b) even if single-case probabilities make sense, they need not be governed by statistical norms because such norms are "content-blind" and can conflict with conversational norms; (c) conflicting statistical norms exist. I try to clear up certain misunderstandings that may have hindered progress in this debate. Gigerenzer's main point turns out to be far less extreme than the position of "normative agnosticism" attributed to him by Kahneman and Tversky: Gigerenzer is not denying that norms appropriate for single-case judgments exist, but is rather complaining that the existence and the nature of such norms have been dogmatically assumed by the heuristics and biases literature. In response to this complaint I argue that single-case probabilities (a) make sense and (b) are governed by probabilistic norms, and that (c) the existence of conflicting statistical norms may be less widespread and less damaging than Gigerenzer thinks.

Bias↗

PubMed2000

Distinguishing minisatellite mutation from non-paternity by MVR-PCR.

Minisatellite variant repeat (MVR) mapping using the polymerase chain reaction (PCR) was devised to map the interspersion pattern of subtle variant repeats along minisatellite tandem arrays. MVR-PCR has revealed enormous diversity of allele structures at several loci, far more than can be resolved by allele length analysis. We have reported the application of MVR-PCR at minisatellite MS32 (D1S8) and MS31A (D7S21) in a paternity case lacking a mother and showed that it resulted in higher paternity probabilities than for a set of 12 other DNA markers including six STRs. Hypervariable minisatellites like MS32 and MS3lA can however, show significant germline mutation rates to new length alleles which can generate false exclusions in paternity cases although paternity cases showing mutant paternal alleles at more than one locus will be rare when several MVR loci are examined. Detailed knowledge of mutation processes coupled with MVR analysis of allele structure can help distinguish mutation from non-paternity. We now show how similar mutant alleles are to their progenitors using both real and simulated data, and demonstrate how MVR-PCR can be used to identify mutant paternal allele in paternity cases showing apparent exclusions.

Bias↗