In summary, these results indicate that there is a large disparity in the likelihood of observing significant differences in metastasis risk among breast cancer data sets using arbitrary gene expression levels as a classifier.
In the present study, we show that there are marked differences in the likelihood of observing a positive association between the expression patterns of a set of genes and metastasis across four breast cancer global gene expression data sets.
To gain insight into the extent of potential systematic differences in obtaining statistically significant results across breast cancer gene expression data sets, we ascertained whether there were intrinsic differences in the likelihood of observing an association between the expression of a selected set of genes and metastasis using four well-characterized data sets.
Taken together, these observations will help to improve our understanding of the diverse clinical course of SSc and permit refinement of existing outcome measures for the design of future clinical trials, in which the likelihood of observing a positive treatment effect with the drugs at our disposal will be maximized.
Although other routes of infection, such as inoculation injury, are possible for these 2 patients, genetic diversity of B. pseudomallei in even a small area in Thailand indicates that the likelihood of observing an identical genotype for an isolate in water and an isolate from a human that was acquired by soil inoculation is probably low (12 ).
In this case, the likelihood of observing the threshold measurement follows the cumulative density of the lower tail of the normal distribution.
This model could potentially be used in the design of clinical trials for drugs that affect the growth of AAAs in men by simulating the likelihood of observing a significant change in AAA growth for a proposed trial protocol (e.g., number of patients needed, number and timing of observations per patients required, and duration of study).
The rationale for choosing azacR, wt-FV3, and SsMeV was thus to have isolates with variable presumed infectivity (a situation likely to occur in nature) and consequently to increase the likelihood of observing variation in infection outcomes and to reveal genotypic interactions.
We chose both a mouse genetic model and a rat pharmacological model of elevated Vmax in order to increase the likelihood of observing reproducible effects by minimizing potential flaws/changes in a single model, such as developmental alterations in the genetic model, off-target modifications in the pharmacological model, or species effects that could be driving the observed changes.
The estimated magnitude of this relation is quite strong, with the presence of a blockholder in some cases decreasing the likelihood of observing another blockholder by a factor of more than one third.
