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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/9234完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉仁沛 | |
| dc.contributor.author | Tsung-Cheng Hsieh | en |
| dc.contributor.author | 謝宗成 | zh_TW |
| dc.date.accessioned | 2021-05-20T20:13:58Z | - |
| dc.date.available | 2009-07-24 | |
| dc.date.available | 2021-05-20T20:13:58Z | - |
| dc.date.copyright | 2009-07-24 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-07-21 | |
| dc.identifier.citation | Hanning J, Iyer HK, Patterson P. (2006). Fiducial generalized confidence intervals. J. Am. Stat. Assoc. 101: 254-269.
ICH Expert Working Group, International Conference on Harmonization Tripartite Guideline Q2A: Test on Validation of Analytical Procedures. (1995). Tholen DW, Kroll M, Astles JR, Caffo AL, Happe TM, Krouwer J, Lasky F. EP6A: Evaluation of the Linearity of Quantitative Measurement Procedures: A Statistical Approach; Approved Guideline, Clinical Laboratory Standard Institute, Wayne, PA, U.S.A. 2003. Tsui KW, Weerahandi S. (1989). Generalized P-values in Significance Testing of Hypotheses in the Presence of Nuisance Parameters. J. Am. Stat. Assoc.; 84: 602-607. Weerahandi S. (1993). Generalized Confidence Intervals. J. Am. Stat. Assoc. 88: 899-905. Wu HJ. (2008). A study on statistical methods for evaluation of linearity in assay validation. Unpublished Master Thesis, Division of Biometry, Department of Agronomy, National Taiwan University. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/9234 | - |
| dc.description.abstract | 在檢定確效性的評估中，線性是最重要的特性之ㄧ。目前，評估線性的統計方法是由 Clinical Laboratory Standard Institute (CLSI) EP6-A準則 所提出。這個方法直接比較點估計值和允許區間並且完全忽略點估計值的抽樣誤差。另一個評估線性的方法是由 Kroll, et al. (2000) 所提出，他使用了線性平均離散程度 (ADL) 當作統計檢定方法，但是卻使用了不正確的統計假設與對應之統計檢定方法。因此，現有兩個方法的型一誤差可能會因而變大而無法做出正確評估。我們提出了雙尾檢定方法與 corrected Kroll’s 方法來改善現有方法之缺點。另一方面，我們亦建議了一個以廣義樞紐量(Generalized Pivotal Quantity, GPQ) 為基礎的 ADL 方法來克服由於ADL的機率分布存在未知之參數 (nuisance parameter)，而使得型一誤差受到未知之參數干擾的問題。
此外，我們亦建議了兩個新的用來評估線性程度的聚合型測度 (aggregate measure)。其中 SSDL 代表線性離散程度平方和。另一方面，CVDL則同時考量了變異程度的影響，而定義為相對於變異之線性平均離散程度平方和。經由模擬研究結果顯示，我們所提出各個方法皆比現有由 CLSI EP6-A 準則 與 Kroll et al. 所提出之方法不僅能有效控制型一誤差並且達到一定水準的檢定力。最後，針對我們提出的方法，也利用了數個例子進行資料分析與方法間之比較。 | zh_TW |
| dc.description.abstract | Linearity is one of the most important characteristics for evaluation of the accuracy in assay validation. The current estimation method for evaluation of the linearity recommended by the Clinical Laboratory Standard Institute (CLSI) guideline EP6-A (Tholen et al., 2003) directly compares the point estimates with the pre-specified allowable limit and completely ignores the sampling error of the point estimates. An alternative method for evaluation of linearity proposed by Kroll, et al. (Kroll, 2000) considers the statistical testing procedure based on the average deviation from linearity (ADL). However this procedure is based on the inappropriate formulation of hypothesis for evaluation of the linearity. Consequently, the type I error rates of both current methods may be inflated for inference of linearity. Therefore, we propose a two one-sided test (TOST) procedure and a corrected Kroll’s procedure as the more appropriate procedure for assessment of linearity. On the other hand, for the purpose to overcome the issue raised by the unknown nuisance parameters of the distribution of ADL, the GPQ-based ADL procedure is also proposed.
In addition, we introduced two new alternative measures SSDL and CVDL which are defined as the sum of square of deviations from linearity and the deviations scaled by the variability, respectively, as the aggregate criteria for assessment of linearity. Unlike ADL and SSDL, CVDL can consider linearity and repeatability of an assay method simultaneously. The relationship among the dofferent aggregate criteria is discussed. The simulation studies are conducted to empirically investigate the size and power among the current and proposed methods. The simulation results show that all proposed methods can adequately control size better than the current methods. Numerical examples are also used to illustrate the application of the proposed methods. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-20T20:13:58Z (GMT). No. of bitstreams: 1 ntu-98-D94621202-1.pdf: 641403 bytes, checksum: f7a153da4ba594a7f6c9feb956b544af (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | CONTENTS
CHAPTER 1 Introduction....................................1 CHAPTER 2 Literature Review...............................8 2.1 Experiment Design.....................................8 2.2 Evaluation Procedure of CLSI Guideline EP6-A..........9 2.3 Uncorrected Kroll’s Method..........................10 2.4 Summary..............................................12 CHAPTER 3 Criterion for Assessing Linearity..............14 3.1 Disaggregate Criterion...............................14 3.2 Aggregate Criterion..................................15 3.2.1 Average Deviation from Linearity (ADL).............15 3.2.2 Sum of Squares of Deviations from Linearity (SSDL).16 3.2.3 Coefficient of Variation of the Deviations from Linearity (CVDL).........................................16 3.3 Summary..............................................17 CHAPTER 4 TOST Procedure and Corrected Kroll’s Method...19 4.1 Two One-sided Test (TOST) Procedure..................19 4.2 Corrected Kroll’s Method............................21 4.3 Simulation Study.....................................21 4.4 Numerical Example....................................26 4.5 Summary..............................................33 CHAPTER 5 General Pivotal Quantity Approach of ADL.......37 5.1 General Pivotal Quantity (GPQ).......................37 5.2 General Pivotal Quantity of ADL......................38 5.3 Generalized Confidence Interval of ADL...............42 5.4 Statistical Testing Procedure........................43 5.5 Simulation Study.....................................43 5.6 Numerical Example....................................47 5.7 Summary..............................................50 CHAPTER 6 Alternative Aggregate Criterion – Sum of Square of the Deviation from Linearity (SSDL)...................56 6.1 SSDL and Statistical Hypothesis......................56 6.2 General Pivotal Quantity of SSDL.....................57 6.3 Generalized Confidence Interval of SSDL..............57 6.4 Statistical Testing Procedure........................58 6.5 Simulation Study.....................................58 6.6 Numerical Example....................................61 6.7 Summary..............................................65 CHAPTER 7 Alternative Aggregate Criterion – Sum of Square of the Deviation from Linearity Related to the Variation (CVDL)...................................................68 7.1 CVDL and Statistical Hypothesis......................68 7.2 General Pivotal Quantity of CVDL.....................69 7.3 Generalized Confidence Interval of CVDL..............70 7.4 Statistical Testing Procedure........................71 7.5 Simulation Study.....................................71 7.6 Numerical Example....................................73 7.7 Summary..............................................79 CHAPTER 8 Discussion and Summary.........................80 8.1 Relationship among the Aggregate Criteria............80 8.2 Comparison by Simulation Study.......................81 8.3 Numerical Example....................................87 8.4 Summary..............................................89 CHAPTER 9 Concluding Remarks.............................90 9.1 Conclusion...........................................90 9.2 Other Application and Future Research................94 Bibliography.............................................97 Appendix.................................................98 LIST OF TABLES Table 4.3.1 Specifications of parameters for size (Uncorrected Kroll vs. Corrected Kroll and Estimation Method vs. TOST).........................................24 Table 4.3.2 Specifications of parameters for power (Uncorrected Kroll vs. Corrected Kroll and Estimation Method vs. TOST).........................................25 Table 4.3.3 Results of empirical sizes (Uncorrected Kroll vs. Corrected Kroll and Estimation Method vs. TOST)......27 Table 4.3.4 Results of empirical powers (Uncorrected Kroll vs. Corrected Kroll and Estimation Method vs. TOST)......28 Table 4.4.1 Measurement of β-HCG.........................30 Table 4.4.2 Summary of results of regression analyses of β-HCG......................................................31 Table 4.4.3 Mean differences between the best-fitted curve and simple linear regression equation of β-HCG...........34 Table 4.4.4 Results of the linearity by four different methods of β-HCG.........................................35 Table 5.5.1 Empirical sizes (Corrected Kroll’s method vs. GPQ-based ADL method)..45 Table 5.5.2 Empirical powers with the true ADL=0.005 (Corrected Kroll’s method vs. GPQ-based ADL method).....46 Table 5.6.1 Measurement of calcium.......................51 Table 5.6.2 Summary of results of regression analyses for the example of calcium...................................52 Table 5.6.3 Mean differences between the best-fitted curve and simple linear regression equation for the example of calcium..................................................54 Table 5.6.4 Results of the linearity evaluation for the example of calcium by corrected Kroll’s and GPQ-based ADL methods..................................................55 Table 6.5.1 Empirical sizes (corrected Kroll’s method vs. GPQ-based SSDL method)...................................60 Table 6.5.2 Empirical powers with the true ADL=0.005 (corrected Kroll’s method vs. GPQ-based SSDL method)....62 Table 6.6.1 Results of the linearity evaluation for the example of calcium by the corrected Kroll’s and GPQ-based SSDL methods.............................................66 Table 7.5.1 Empirical sizes (corrected Kroll’s method vs. GPQ-based CVDL method)...................................74 Table 7.5.2 Empirical powers with the true ADL=0.005 (corrected Kroll’s method vs. GPQ-based CVDL method)....75 Table 7.6.1 Results of the linearity evaluation for the example of calcium by corrected Kroll’s and GPQ-based CVDL methods.............................................78 Table 8.2.1 Empirical sizes (GPQ-based SSDL vs. GPQ-based CVDL vs. GPQ-based ADL methods)..........................83 Table 8.2.2 Empirical powers with the true ADL=0.005 (GPQ-based SSDL vs. GPQ-based CVDL vs. GPQ-based ADL methods).84 Table 8.3.1 Results of the linearity evaluation of the example of Calcium by GPQ-based ADL, SSDL and CVDL methods..................................................87 LIST OF FIGURES Figure 1.1 Acceptance of linearity by CLSI EP6-A guideline.................................................3 Figure 1.2 Unacceptance of linearity by CLSI EP6-A guideline.................................................4 Figure 4.4.1 Regression curves for cubic versus linear models of β-HCG..........................................32 Figure 5.5.1 The Empirical powers when standard deviation of normal random error is 0.1, number of solutions is 5, and number of replicates is 3 (Corrected Kroll’s method vs. GPQ-based ADL method)................................48 Figure 5.5.2 The Empirical powers when standard deviation of normal random error is 0.2, number of solutions is 5, and number of replicates is 3 (Corrected Kroll’s method vs. GPQ-based ADL method)................49 Figure 6.5.1 The empirical powers when standard deviation of normal random error is 0.1, number of solutions is 5, and number of replicates is 3 (corrected Kroll’s method vs. GPQ-based SSDL method................63 Figure 6.5.2 The empirical powers when standard deviation of normal random error is 0.2, number of solutions is 5, and number of replicates is 3 (corrected Kroll’s method vs. GPQ-based SSDL method)...............64 Figure 7.5.1 The empirical powers when standard deviation of normal random error is 0.1, number of solutions is 5, and number of replicates is 3 (corrected Kroll’s method vs. GPQ-based CVDL method)...............................76 Figure 7.5.2 The empirical powers when standard deviation of normal random error is 0.2, number of solutions is 5, and number of replicates is 3 (corrected Kroll’s method vs. GPQ-based CVDL method)...............................77 Figure 8.2.1 The empirical powers when standard deviation of normal random error is 0.1, number of solutions is 5, and number of replicates is 3 (GPQ-based SSDL vs. GPQ-based CVDL vs. GPQ-based ADL methods)....................85 Figure 8.2.2 The empirical powers when standard deviation of normal random error is 0.2, number of solutions is 5, and number of replicates is 3 (GPQ-based SSDL vs. GPQ-based CVDL vs. GPQ-based ADL methods)....................86 | |
| dc.language.iso | en | |
| dc.title | 定量體外檢驗試劑線性確校統計評估方法之研究 | zh_TW |
| dc.title | Statistical Evaluation of the Linearity for Quantitative in Vitro Diagnostic Devices | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.coadvisor | 蕭金福 | |
| dc.contributor.oralexamcommittee | 周賢忠,嵇允嬋,張啟仁,薛慧敏,廖振鐸,季瑋珠 | |
| dc.subject.keyword | 允許區間,線性,量化分析的實驗方法,廣義樞紐量, | zh_TW |
| dc.subject.keyword | Allowable Limit,Linearity,Quantitative analytical laboratory methods,Generalized Pivotal Quantity, | en |
| dc.relation.page | 123 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2009-07-21 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 農藝學研究所 | zh_TW |
| 顯示於系所單位： | 農藝學系 | |
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