Identifying outlier subjects in bioavailability trials using generalized studentized residuals
This paper discusses several outlier detection methods for bioavailability trials, particularly based on residuals. By considering a simplified model of standard crossover model, which is commonly used in bioavailability trials, we propose an outlier detection procedure based on the generalized stud...
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Penerbit Universiti Kebangsaan Malaysia
2023
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my-ukm.journal.221642023-09-06T04:10:54Z http://journalarticle.ukm.my/22164/ Identifying outlier subjects in bioavailability trials using generalized studentized residuals Lim, F.P. Wong, L.L. Yap, H.K. Yow, K.S. This paper discusses several outlier detection methods for bioavailability trials, particularly based on residuals. By considering a simplified model of standard crossover model, which is commonly used in bioavailability trials, we propose an outlier detection procedure based on the generalized studentized residuals (SR3) and compare its ability of detecting the possible outlying subjects with two existing procedures, which are carried out based on the classical studentized residual (SR1) and studentized residual using median absolute deviation (SR2). The performances of these procedures in detecting outlying subject are presented via an extensive simulation study. The results show that the proposed procedure SR3 performs more powerful than that using SR1, and as well as the procedure using SR2 for outlier detection. As an illustration, these procedures are implemented on a real dataset from bioavailability study, namely, the area under the curve (AUC) dataset for two erythromycin formulations. Penerbit Universiti Kebangsaan Malaysia 2023 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/22164/1/SL%2019.pdf Lim, F.P. and Wong, L.L. and Yap, H.K. and Yow, K.S. (2023) Identifying outlier subjects in bioavailability trials using generalized studentized residuals. Sains Malaysiana, 52 (5). pp. 1581-1593. ISSN 0126-6039 http://www.ukm.my/jsm/index.html |
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This paper discusses several outlier detection methods for bioavailability trials, particularly based on residuals. By considering a simplified model of standard crossover model, which is commonly used in bioavailability trials, we propose an outlier detection procedure based on the generalized studentized residuals (SR3) and compare its ability of detecting the possible outlying subjects with two existing procedures, which are carried out based on the classical studentized residual (SR1) and studentized residual using median absolute deviation (SR2). The performances of these procedures in detecting outlying subject are presented via an extensive simulation study. The results show that the proposed procedure SR3 performs more powerful than that using SR1, and as well as the procedure using SR2 for outlier detection. As an illustration, these procedures are implemented on a real dataset from bioavailability study, namely, the area under the curve (AUC) dataset for two erythromycin formulations. |
| format |
Article |
| author |
Lim, F.P. Wong, L.L. Yap, H.K. Yow, K.S. |
| spellingShingle |
Lim, F.P. Wong, L.L. Yap, H.K. Yow, K.S. Identifying outlier subjects in bioavailability trials using generalized studentized residuals |
| author_facet |
Lim, F.P. Wong, L.L. Yap, H.K. Yow, K.S. |
| author_sort |
Lim, F.P. |
| title |
Identifying outlier subjects in bioavailability trials using generalized studentized residuals |
| title_short |
Identifying outlier subjects in bioavailability trials using generalized studentized residuals |
| title_full |
Identifying outlier subjects in bioavailability trials using generalized studentized residuals |
| title_fullStr |
Identifying outlier subjects in bioavailability trials using generalized studentized residuals |
| title_full_unstemmed |
Identifying outlier subjects in bioavailability trials using generalized studentized residuals |
| title_sort |
identifying outlier subjects in bioavailability trials using generalized studentized residuals |
| publisher |
Penerbit Universiti Kebangsaan Malaysia |
| publishDate |
2023 |
| url |
http://journalarticle.ukm.my/22164/1/SL%2019.pdf http://journalarticle.ukm.my/22164/ http://www.ukm.my/jsm/index.html |
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1778162563289186304 |
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13.211869 |