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Sensitivity analyses were conducted to assess the variability of the disease impact for different combinations of disease parameter values.
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The set-up is usually summarized by a four-fold table where each cell represents the absolute number of individuals for the different combinations of outcome (disease D+ vs. no disease D-) and test result (positive T+ vs. negative T-) (see Additional file 1: Appendix A, Table A1).
Comorbidity encompassed many different combinations of chronic diseases.
The numbers of shared coexpressed sets show degree of association among different combinations of brain diseases in Figure 1.
By using different combinations of these 4 disease indicators, we designed 5 automated classifiers for the presence of AMD on the basis of the generalized linear model regression framework.
This allowed much greater flexibility in estimating utility values for different combinations of toxicities and disease states.
Moreover, for 120 persons reporting more than one indicator disease, we performed the chi-square test to study the association between different combinations of multiple indicator diseases and late presentation.
The number of proteins/genes common to different combinations of the examined diseases is shown in Figure 1.
Our data indicate that different combinations of mutations in disease-causing genes may be responsible for the various phenotypes of SAMP strains.
It reveals different combinations of various sites and disease types which result from sequential genetic changes in multiple intracellular pathways and inherent viral infections.
These genome-wide screening studies explored overlapping QTLs in different genetic combinations between high- or low-susceptibility F2 hybrids, indicating that different combinations of genes may affect disease onset and/or severity [ 9, 24, 29].
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