This study established a framework for the in vitro evaluation of cancer-relevant bioassays for comparisons of insulin analogues, and specifically consolidated earlier studies that determination of the cell-specific IGF-IR/IR ratio is crucial for the interpretation of ranking relative biological activities.
This study has built on that literature and established a cancer-relevant, dose response equivalent and time-dependent framework for evaluating the effect of insulin analogues in vitro and identified the importance of determining the cell-specific IGF-IR/IR ratio for the interpretation of ranking relative biological activities.
Apart from the problems described above related to case-mix and extreme risks, sampling and measurement uncertainty have profound effects on the interpretation of ranks.
While the rank of the metabolites provides valuable information, the robustness of this rank is just as important as it determines the limits of the reliable interpretation of the rank.
The local interpretation of the rank-order method does satisfy regularity and IIA but fails to satisfy the consistency condition because to determine the ranking over a set of actual fates A, only the lineages' propensities restricted to A are taken into account (no information about their propensities over the potential-but-not-actually-considered fates is used).
Because for each given profile the global interpretation of the rank-order method generates a complete weak ordering over the whole of X, which will be used to determine the tissue fate over the set of actual fates, the corresponding lineage-tissue mapping satisfies the consistency condition but fails to satisfy IIA and regularity.
With respect to the conceptual difference between X and A, we can proceed with our analysis in two ways [ 28]: The global interpretation of the rank-order principle would assume that it is possible to know the propensities for the whole of X.
In support of our focus on developmental genes, pathway analysis of recent genome-wide association studies, which so far have yielded few T2D candidate genes, provided an integrated interpretation of the highest ranked risk genes for T2D [ 97].
The Bayesian framework for network meta-analyses also allows for the probabilistic interpretation of uncertainty and ranking of interventions [ 39].
The combined interpretation of Table 3 (ranking of variable importance) and Table 4 (proportion of primary diagnoses) revealed that Cluster n#1 (n = 72) was characterized by a high prevalence of LMP as the primary diagnosis, the absence of internal derangement (DDWOR and DDWR) and myofascial pain (CMP and MMP), and a high prevalence of bruxism as the secondary diagnoses.
The interpretation of within-group ranking of the importance of variables in the clustering solutions resulted in the following characterization of clusters: chronic facial pain (n = 36), acute muscle pain (n = 125), acute articular pain (n = 75) and chronic articular impairment (n = 121).
