This allowed randomization also for the presentation of the empty compartment in binary trials.
Although we did not observe any difference in the average time females spent with Y vs. S in the binary trials (paired t test: t24 = −0.74, p = 0.47; time with Y: mean ± SD = 281 ± 279 s; time with S mean ± SD = 225 ± 185 s), a strong bias in individual preference emerged.
This probit model was extended to deal with p >> n in the genomic era by González-Recio and Forni (2011) and Villanueva et al. (2011) for binary trials, and by Wang et al. (2012) and Montesinos-López et al. (2015) for more than two ordinal categories.
When several minutes, hours, or days intervene between the prospective memory instructions and appearance of a prospective memory cue, task performance is likely to reflect prospective memory proper but this single binary outcome trial is inefficient and much less reliable than continuous indices used to measure retrospective memory [1], [12], [16].
Choices were binary and trials with the three pairs of foods (A B, B C and C-A) were interleaved pseudo-randomly.
In an editorial in the journal Circulation, Loscalzo [ 7] proposed a binary classification: trials designated a priori as pilots, and those redefined a posteriori.
The collision probability was measured empirically, using trials in the binary case and trials in the -ary case.
To calculate the pooled risk ratio (RR) and 95% CIs of binary outcomes (mortality) trial data were combined using the Mantel-Haenszel estimator with estimation of variances according to the DerSimonian and Laird random-effect model [ 19].
In this study, the direct comparisons of B versus A and C versus A were performed on the basis of trials with a binary outcome for each trial participant (i.e., participant experienced/did not experience the event of interest).
We propose to move from a binary approach in trials to a more quantitative approach, based on differences in cumulative glycemic exposure.
