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The definition of optimality criteria and the design of solution methods must account for the random nature of the travel-time on each link.

Analysis methods must account for clustering or the 95%% confidence intervals will be too narrow.

Furthermore, our methods must account for the inherently multidisciplinary nature of diagnostic error research.

Methods must account for the presence of heterogeneity in effects, which necessitates a more granular assessment of outcomes.

Evaluation methods must account for the presence of heterogeneity in effects of disease management, produced by differences in interventions and targeted patients [ 10- 13].

These are post hoc meta-analyses, extracting published results and effect sizes from the literature; given the unknown number of unpublished analyses, these methods must account for the "desk drawer" phenomenon by various means.

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Senescence modeling in natural populations requires modifications to the above methods because one must account for individuals whose date of senescent death is not known.

Methods must therefore account for the random effects of individual variation in a way not otherwise necessary.

Such gene set testing methods must specifically account for the correlation between gene-level test statistics.

Any method predicting vaccine effectiveness must account for the genetic diversity of MenB strains, which, through gene regulation, phase variation, and sequence diversity, can affect the levels of expression of these surface antigens [ 33].

The statistical methods used to analyze the data must account for the reliability of the data, piece by piece.

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