Exploratory data analysis for outlier detection in bioequivalence studies

• Autorzy: Mogoş B.
• Języki publikacji: EN

Abstrakty

EN

Exploratory Data Analysis techniques are recognized as useful tools in outlier detection through visual representations. One limitation of this direction is the lack of studies concerning the reliability of the visual interpretation. In this paper we propose a method that combines an Exploratory Data Analysis technique, Andrews curves, with a statistical approach which can be applied to automatically classify the data. Using a simulation study we show that the results provided by the Andrews curves approach are markedly superior to the estimates distance test (the best proposed method for detecting outliers revealed in the literature) for the crossover bioequivalence design.

Słowa kluczowe

EN

outlier detection; bioequivalence studies; crossover design; Andrews curves; estimates distance

PL

wykrywanie wartości oddalonej; badanie biorównoważności; krzywe Andrewsa; szacowanie odległości

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2013
• Tom: Vol. 33, no. 3
• Strony: 164--170
• Opis fizyczny: Bibliogr. 12 poz., tab., wykr.

Twórcy

autor

Mogoş B.

• Faculty of Mathematics and Computer Science, University of Bucharest, Romania

Bibliografia

• [1] Chow SC, Tse SK. Outliers detection in bioavailability/ bioequivalence studies. Stat Med 1990; 9: 549–58.
• [2] Ramsay T, Elkum N. A comparison of four different methods for outlier detection in bioequivalence studies. J Biopharm Stat 2005; 15: 43–5.
• [3] Andrews DF. Plots of high-dimensional data. Biometrics 1972; 28: 125–36.
• [4] Enăchescu D, Enăchescu C. A new approach for outlying records in bioequivalence trials. In: Sakalauskas L, Skiadas C, Zavadskas EK, editors. Selected papers of the 13th International Conference on Applied Stochastic Models and Data Analysis. Vilnius, Lithuania: VGTU Press ‘‘Technika’’; 2009. pp. 250–7. ISBN 978-9955-28-463-5.
• [5] Rasheed A, Junaid S, Ahmad T. Detection of outliers in bioequivalence studies data analysis with Williams design. J Pharm Nutr Sci 2011; 1: 61–7.
• [6] Barnett V, Lewis T. Outliers in statistical data, 3rd ed., John Wiley & Sons; 1994.
• [7] Embrechts P, Herzberg AM. Variations of Andrew's plots. Int Stat Rev 1991; 59: 175–94.
• [8] Martinez WL, Martinez AR. Computational statistics handbook with MATLAB. Boca Raton/London/New York/ Washington, DC: Chapman & Hall/CRC; 2002.
• [9] Almendra V, Roman B. Using exploratory data analysis for fraud elicitation through supervised learning. In: Wang D, Negru V, Ida T, Jebelean T, Petcu D, Watt S, Zaharie D, editors. IEEE Proceedings of SYNASC 2011, the 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. 2011. pp. 251–4. ISBN 978-0-7695-4630-8.
• [10] Vaduva I. Variance analysis. Bucharest: Editura Tehnică; 1970 [in Romanian].
• [11] Rousseeuw PJ, Hubert M. Robust statistics for outlier detection. Wiley Interdiscip Rev Data Min Knowl Discov 2011; 1: 73–9.
• [12] Chandola V, Banerjee A, Kumar V. Anomaly detection – a survey. J ACM Comput Surv (CSUR) 2009; 41: 1–58.