For all reported comparisons, the likelihood of Type I error was reduced based on cluster size threshold estimation [27], [28] involving a Monte Carlo simulation calculating the likelihood to obtain different cluster sizes.
When one study examines treatment differences across multiple risk factors (multiple comparisons), the likelihood that a study with a reported subgroup effect has at least one false positive result increases, thereby eroding the reliability of all positive findings.
This comparison was made for ever reporting an event, that is, a comparison of the likelihood that subjects would report an event at any point during follow-up vs. the likelihood that an official record would be found.
The risk is minimized through correctly assigning experimental data to two decision regions based on the comparison of the likelihood ratio with a decision threshold.
The comparison of the likelihood ratio of model 1 versus model 0 reveals that there is a significant effect on health conditions due to area characteristics.
Comparison of the likelihood scores of all the phonemes for the two age groups show that there is a systematic mismatch in the acoustic space of the two age groups.
The best-fit model was determined using a likelihood ratio test for each model comparison, thus the likelihood of positive selection could be evaluated [ 70].
Our purpose is to provide a fair comparison of the likelihood ratio (LR) test and the Wald test versus diagnostic accuracy (AUC) tests.
Hypergeometric mean analysis was performed as described by Chung and colleagues [ 22]; this comparison gives the likelihood of finding co-occurrences between these gene sets by chance.
