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Multiscale and multilevel influences in cancer prevention and control

Multiscale and multilevel influences in cancer prevention and control. Multilevel Socioecological Influences “Above the Skin”. New VV & UQ Approaches are needed

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Multiscale and multilevel influences in cancer prevention and control

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  1. Multiscale and multilevel influences in cancer prevention and control Multilevel Socioecological Influences “Above the Skin” New VV & UQ Approaches are needed • Currently, all high level “simulators” use first order input (only account for mean and variance of input data) and first order output (mean and variance propagates through). • Uncertainties propagation across different models are not accounted for. • New methods should be devised to incorporate correlations among all data sets. Nested Hierarchies of Socioecological and Biological Influences on Patient Behavior Missing Mutiscale Biological Influences “Under the Skin” Uncertainty quantification (UQ) Uncertainty propagation (UP) Design under uncertainty • Currently, mean and variance serve as input parameters for a given model. • A simulation may be based on a single or a set of different models with independent data sets. • Predictions are based on a combination of model observations. Newer uncertainty quantification techniques: • Quadrature-based collocation (UP) • Polynomial chaos expansion (UP, UQ) • Principal component analysis (UQ) • Robust, reliability-based design • Bayesian model updating (UQ, prediction) • Monte Carlo methods Morrissey J P et al. J Natl Cancer Inst Monogr 2012;2012:56-66

  2. Can we do better? INPUT INPUT INPUT Data 2 Data 3 Data 1 Model 2 Model 3 Model 1 in silico in vitro in vivo in silico in vitro in vivo in silico in vitro in vivo OUTPUT OUTPUT Modeling and Simulation OUTPUT Covariates / Correlations / Relations Prediction with mean and variability Confidence increases with more data/models • In current models, the mean and variance of a given data set are the input parameters for a simulation. • Many models exist, each having some form of regulatory evaluation. • Each model is based on independent data sets. • A prediction (with a given mean and variance) can be made based on simulations from either a single model or a set of different models.

  3. High level simulators use deterministic input & do not account for uncertainties Modular structure of MEDICI-PK Physiologically based pharmacokinetic (PBPK) modeling AAPS AdvPharmaceuSci1.483'11 DDT11.800'06 * Red arrows indicate output from the models Missing Phy-SIM System Architecture Uncertainty quantification (UQ) Uncertainty propagation (UP) Design under uncertainty • Quadrature-based collocation (UP) • Polynomial chaos expansion (UP, UQ) • Principal component analysis (UQ) • Robust, reliability-based design • Bayesian model updating (UQ, prediction) • Monte Carlo methods CompMethProgBiomed inpress '12

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