Missing data in model-based pharmacometric applications: points to consider. - Wikidata - wikimedia - TriplyDB

Missing data in model-based pharmacometric applications: points to consider.

Missing data in model-based pharmacometric applications: points to consider.

http://www.wikidata.org/entity/Q33706156

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Population pharmacokinetics of bevacizumab in cancer patients with external validation Comparison of methods for handling missing covariate data.Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group Combining patient-level and summary-level data for Alzheimer's disease modeling and simulation: a β regression meta-analysis.Model-Based Approach for Optimizing Study Design and Clinical Drug Performances of Extended-Release Formulations of Methylphenidate for the Treatment of ADHD.Performance of nonlinear mixed effects models in the presence of informative dropout.A novel metric to assess the clinical utility of a drug in the presence of efficacy and dropout information.Modelling and simulation of the Positive and Negative Syndrome Scale (PANSS) time course and dropout hazard in placebo arms of schizophrenia clinical trials.Population Pharmacokinetics of Liposomal Irinotecan in Patients With Cancer.A Pharmacometric Analysis of Patient-Reported Outcomes in Breast Cancer Patients Through Item Response Theory.

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P2860

Missing data in model-based pharmacometric applications: points to consider.

http://www.wikidata.org/entity/Q33706156

description

2010 nî lūn-bûn

@nan

2010 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած

@hyw

2010 թվականի սեպտեմբերին հրատարակված գիտական հոդված

@hy

2010年の論文

@ja

2010年論文

@yue

2010年論文

@zh-hant

2010年論文

@zh-hk

2010年論文

@zh-mo

2010年論文

@zh-tw

2010年论文

@wuu

name

Missing data in model-based pharmacometric applications: points to consider.

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Missing data in model-based pharmacometric applications: points to consider.

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Missing data in model-based pharmacometric applications: points to consider.

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Missing data in model-based pharmacometric applications: points to consider.

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Missing data in model-based pharmacometric applications: points to consider.

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Missing data in model-based pharmacometric applications: points to consider.

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The Journal of Clinical Pharmacology

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Missing data in model-based pharmacometric applications: points to consider

@en

P2093

Daniel F Heitjan

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Jae Eun Ahn

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James A Rogers

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Marc R Gastonguay

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Patanjali Ravva

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P2860

Longitudinal data analysis for discrete and continuous outcomes

Ways to fit a PK model with some data below the quantification limit.

A joint model for nonlinear longitudinal data with informative dropout.

Multiple imputation for model checking: completed-data plots with missing and latent data.

Likelihood based approaches to handling data below the quantification limit using NONMEM VI.

Handling data below the limit of quantification in mixed effect models.

Handling drop-out in longitudinal studies.

Modelling placebo response in depression trials using a longitudinal model with informative dropout.

Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development.

Extensions to the visual predictive check to facilitate model performance evaluation.

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63S-74S

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10.1177/0091270010378409

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9 Suppl

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50

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Jonathan French

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2010-09-01T00:00:00Z

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20881220

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