ArticlePharmaceutical research2014
Handling missing data in a duloxetine population pharmacokinetic/pharmacodynamic model - imputation methods and selection models.
Article in Pharmaceutical research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Accounting for dropout in xenografted tumour efficacy studies: integrated endpoint analysis, reduced bias and better use of animals.Cancer chemotherapy and pharmacology · 2016Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeIn pharmacokinetic (PK)/pharmacodynamic (PD) modelling and simulations (M&S), omitting dropouts can cause inaccuracies in parameter estimation and clinical trial simulations (CTS). This study examines the impact of different imputation methods for missing data on the interpretation of model results, as well as develops a selection model (where dropout and efficacy are jointly modelled) for use in CTS.
methodsMissing data were imputed using single and multiple imputation and pattern mixtures methods for a previously reported duloxetine PK/PD model. The probability of dropout was described in the selection model and CTS was conducted with a hypothetical drug to examine the impact of dropout on trial results.
resultsThe study completion rate was 75% and dropouts were not random. Model parameters obtained with different imputation methods were mostly within 40% (range 0 to 63%) compared to the model without dropouts. CTS showed 0.3 points lower median pain scores and 3% lower coefficient of variation over the 12-week simulations when dropout was included.
conclusionsMissing data had little impact on the original population PK/PD analyses. Sensitivity analyses for dropouts should be conducted in M&S exercises. The utility of selection models in CTS was explored via a hypothetical case study.
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Registered trials
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