Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models us...
Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling
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Publisher
New York: Springer US
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Language
English
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New York: Springer US
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Contents
Taking parameter uncertainty into account is key to make drug development decisions such as testing whether trial endpoints meet defined criteria. Currently used methods for assessing parameter uncertainty in NLMEM have limitations, and there is a lack of diagnostics for when these limitations occur. In this work, a method based on sampling importa...
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Full title
Improving the estimation of parameter uncertainty distributions in nonlinear mixed effects models using sampling importance resampling
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TN_cdi_swepub_primary_oai_DiVA_org_uu_303629
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_swepub_primary_oai_DiVA_org_uu_303629
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ISSN
1567-567X,1573-8744
E-ISSN
1573-8744
DOI
10.1007/s10928-016-9487-8
