In biomedical research, where studies often involve multifaceted datasets and complex statistical models, sensitivity analysis acts as a safeguard against uncertainties and biases, providing researchers with a clearer picture of the robustness of their findings.
Apr 19, 2024
This article discusses the application of sensitivity analysis (SA) in biomedical models. Sensitivity analysis is widely applied in physics, chemistry, ...
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Sensitivity Analysis (SA) can be defined as the study of how uncertainty in a model's output can be apportioned to different sources of uncertainty in the model ...
Jan 12, 2020 · Sensitivity analysis is an integral part of the mathematical modelling cycle and is placed within model analysis module.
Dec 20, 2023 · The aim of this study was to propose an efficient correlated global sensitivity analysis method by applying a surrogate model-based approach.
Abstract—This article discusses the application of sensitiv- ity analysis (SA) in biomedical models. Sensitivity analysis is widely applied in physics, ...
In this review paper, we describe the most frequently used sensitivity techniques, discussing their advantages and limitations, before applying each method to ...
Jul 16, 2013 · Sensitivity analyses play a crucial role in assessing the robustness of the findings or conclusions based on primary analyses of data in clinical trials.
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We present an overview of sensitivity analysis methods, approaches for extending such methods to a multiscale setting, and examples of how sensitivity analysis ...
Jan 12, 2020 · Sensitivity analysis studies how a change in a model's specific output can be attributed to different sources of uncertainty in its (likely) ...