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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 蘇秀媛 | |
dc.contributor.author | Hsin-Neng Hsieh | en |
dc.contributor.author | 謝鑫能 | zh_TW |
dc.date.accessioned | 2021-06-08T06:58:02Z | - |
dc.date.copyright | 2009-07-16 | |
dc.date.issued | 2009 | |
dc.date.submitted | 2009-07-12 | |
dc.identifier.citation | Bibliography
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/25985 | - |
dc.description.abstract | 本研究主要探討廣義推論(generalized inference)在生物統計之應用。研究主要分成三個部份。體外溶離率試驗已被建議用來替代藥物生體相等性試驗來進行變化前後藥品的相似性之評估。研究的第一部份主要是利用廣義p值(generalized p-value)的概念,在體外溶離率試驗下進行變化前後藥品的相似性之評估。美國食品藥物管理局(FDA)已公告數個有關核准後藥品變化評估的準則。這些公告中所提出評估溶離率剖析曲線(dissolution profile)相似性的標準為根據溶解度平均差均方如相似因子f2,或溶解度平均差絕對值的平均如相似因子g1而訂定。由於相似因子f2與g1的分配複雜不易推導求得,故進行溶離率剖析曲線相似性的檢定就相對困難。因此我們嘗試透過廣義p值(generalized p-value)的概念來進行溶離率剖析曲線的相似性之評估,透過大型的模擬試驗在不同溶解度平均數差、變異數及藥品樣本數下,探討該統計方法之偏差、型I誤差與檢定力並與拔靴法(bootstrap method)的結果做比較。模擬結果發現,以相似因子f2與g1作為評估溶離率剖析曲線相似性的準則時,隨著藥品樣本數愈大,廣義p值與拔靴法的評估表現一樣好。最後透過一個實例以相似因子f2與g1作為評估溶離率剖析曲線相似性的準則時,針對廣義p值法與拔靴法作比較。
接受者操作特徵曲線(receiver operating characteristic curve)簡稱ROC曲線,是常用來評估診斷工具準確性的統計方法。在診斷試驗中,比較新診斷工具與現行標準診斷工具的準確性是一項重要的課題。當黃金標準試驗(gold standard test)存在時,一般常以ROC曲線下面積作為評估診斷工具準確性的指標。然而有些情形下,黃金標準試驗執行費用太昂貴或是黃金標準試驗無法取得。故研究的第二部份主要是討論在常態性假設下,當黃金標準試驗不存在時,利用EM演算法及拔靴法,提出以最大概似法(maximum likelihood method)為基礎的統計程序,針對成對(paired)ROC曲線下面積建立其信賴區間之研究。透過大規模的統計模擬研究可驗證,當黃金標準試驗不存在時,我們提出的區間估計能提供足夠的覆蓋機率(coverage probability)。此外我們亦利用廣義樞紐量(generalized pivotal quantity)來建立當黃金標準試驗不存在時成對ROC曲線下面積的廣義信賴區間(generalized confidence interval)。由統計模擬研究可驗證,當黃金標準試驗不存在時,成對ROC曲線下面積的廣義信賴區間亦能提供足夠的覆蓋機率。最後,利用提出之方法針對胰臟癌(pancreatic cancer)、動脈硬化症(atherosclerosis)等實際診斷資料進行分析。 第三部份利用廣義p值的概念,當變異數異質(heteroscedasticity)時,評估新處理(new treatment)在three-arm試驗中,的非劣性(non-inferiority)。透過模擬試驗,在不同變異數及樣本數組合下,探討提出統計方法之型I誤差與檢定力。一般而論,我們提出的方法之型I誤差均能接近宣稱的水準(nominal level),其檢定力優於Fieller's法與拔靴法。最後,利用提出之統計方法針對兩個實例進行資料分析。 | zh_TW |
dc.description.abstract | The main objective of this dissertation is to use generalized inference on biostatistics. There are three parts in this dissertation. In vitro dissolution testing has been suggested as a surrogate for assessment of bioequivalence between the test and reference formulations for postapproval changes. First is that we use the concept of generalized p-values (GPVs) to assessment of similarity between dissolution profiles. The often used criteria for assessment of dissolution similarity between general profiles are functions of average squared mean differences and absolute mean difference. Because of the complexity of the distributions of estimators of two functions, it is difficult to obtain a test to test the hypothesis of dissolution similarity. Therefore, in first study, the GPVs is applied to construct a test procedure to assess the similarity of dissolution profiles. Simulation results show that when the numbers of dosage units are large, the GPVs testing procedure yields satisfactory results for size and power with f2 and g1 criteria recommended by the U.S. Food and Drug Administration (FDA). Through this simulation study, with the same f2 and g1 criteria, the performance of empirical sizes and empirical power by using GPVs are as good as by using bootstrap method. The proposed method is illustrated with a real example.
The receiver operating characteristic (ROC) curve is a popular statistical tool for the accuracy of diagnostic device. One of primary objectives in a diagnostic test evaluation study is to compare the diagnostic accuracy of the new diagnostic procedure with that of current standard procedure. The second part is that we construct confidence intervals for the difference in paired areas under ROC curves in the absence of a gold standard test. The ROC curves can be used to assess the accuracy of tests measured on ordinal or continuous scales. The most commonly used measure for the overall diagnostic accuracy of continuously valued diagnostic tests is the area under the ROC curve. To estimate such a measure, we require the existence of a gold standard test on the presence of disease status. However a gold standard test may sometimes be too expensive or infeasible to obtain. Therefore, in many medical research studies, the true disease status of the subjects may not be known or available. Under the normality assumption on the diagnostic test results from each group of subjects, based on the expectation-maximization (EM) algorithm in conjunction with a bootstrap method, we propose a maximum likelihood based procedure for construction of confidence intervals for the difference in paired areas under ROC curves in the absence of a gold standard test. In addition, we also propose to use the concept of generalized pivotal quantities (GPQs) to construct generalized confidence intervals (GCIs) for the difference in paired areas under ROC curves in the absence of a gold standard test. Simulation results show that the proposed interval estimation procedures yield satisfactory coverage probabilities and expected lengths. The proposed methods are illustrated with two data examples. The last part is that we propose a generalized inference on assessment non-inferiority of a new treatment in a three-arm trial in the presence of heteroscedasticity. In non-inferiority trials, the goal is to show how an experimental treatment is statistically and clinically not inferior to the active control. The three-arm clinical trial usually recommended for non-inferiority trials by the FDA. The three-arm trial consists of a placebo, reference, and an experimental treatment. In this study, under the normality assumption on the placebo, reference, and an experimental treatment, the GPVs is applied to facilitate non-inferiority tests in a three-arm design. In the situation of heterogeneous group variances, through a simulation study, the GPVs will adequately maintain the alpha level than Fieller's method and bootstrap method. Simulation results also show that the performance of empirical power of GPVs method is as good as that of the Fieller's method and the bootstrap method. Finally, the proposed method is illustrated with two data examples. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T06:58:02Z (GMT). No. of bitstreams: 1 ntu-98-D89621202-1.pdf: 971258 bytes, checksum: d0dc563ba420ce7a401a714f992a9f0a (MD5) Previous issue date: 2009 | en |
dc.description.tableofcontents | Contents
1 Introduction 1 1.1 Assessment of similarity between dissolution profiles 2 1.2 The paired areas under the ROC curves in the absence of a gold standard . 4 1.3 Assessment non-inferiority of a new treatment in a three-arm trial in the presence of heteroscedasticity . 6 1.4 Organization of the dissertation . 7 2 Preliminaries of Generalized Statistical Inference 8 2.1 Generalized p-values. 8 2.2 Generalized confidence intervals . 9 3 A Generalized Inference on Assessment of Similarity between Dissolution Profiles 11 3.1 Criteria and Formulation of Hypothesis for Dissolution Profile Similarity . 12 3.1.1 Similarity criterion based on squared mean difference . 12 3.1.2 Similarity criterion based on absolute mean difference. 13 3.2 Generalized inference on similarity factor f2 and g1 14 3.3 Simulation Study and Results 18 3.3.1 Simulation study on empirical bias. 18 3.3.2 Simulation study on type I error rate 19 3.3.3 Simulation study on power . 23 3.4 Numerical Examples. 26 3.5 Conclusion and Final Remarks. 28 4 Interval Estimation by Using ML-Based Method for the Difference in Paired Areas under the ROC Curves in the Absence of a Gold Standard 32 4.1 The Proposed ML-BasedMethod 33 4.1.1 EM algorithm . 34 4.1.2 Bootstrap method 36 4.2 Simulation Studies 37 4.2.1 Simulation study I 37 4.2.2 Simulation study II42 4.2.3 Simulation study III 42 4.3 Numerical Examples 45 4.3.1 The study of pancreatic cancer serum biomarkers 45 4.3.2 The study of accuracy of magnetic resonance angiography (MRA) readings by two readers 46 4.4 Discussion and Final Remarks 47 5 Generalized Confidence Interval Estimation for the Difference in Paired Areas under the ROC Curves in the Absence of a Gold Standard 50 5.1 Generalized Confidence Interval Estimation 51 5.2 Simulation Studies 55 5.3 Numerical Examples 60 5.3.1 The study of pancreatic cancer serum biomarkers 60 5.3.2 The study of accuracy of magnetic resonance angiography (MRA) readings by two readers61 5.4 Discussion and Final Remarks 62 6 A generalized inference on assessment non-inferiority of a new treatment in a three-arm trial in the presence of heteroscedasticity 63 6.1 Introduction 63 6.2 Statistical Background and Non-inferiority Hypothesis 64 6.3 Generalized inference on the ratio of the differences inmeans 67 6.4 Simulation Study 70 6.4.1 Simulation study on type I error rate 71 6.4.2 Simulation study on power 84 6.5 Numerical Examples 88 6.5.1 A comparative study inmildly asthmatic patients88 6.5.2 Evaluation of themutagenicity example 89 6.6 Conclusion and Final Remarks 91 7 Future Research 92 Bibliography 95 A Appendix A 102 B Appendix B: The estimators in M-step 104 | |
dc.language.iso | en | |
dc.title | 廣義推論在生物統計之應用 | zh_TW |
dc.title | Generalized Inferences on Problems in Biostatistics | en |
dc.type | Thesis | |
dc.date.schoolyear | 97-2 | |
dc.description.degree | 博士 | |
dc.contributor.oralexamcommittee | 廖振鐸,彭雲明,林俊隆,劉力瑜,歐益昌,歐尚靈 | |
dc.subject.keyword | f2因子,g1因子,拔靴法,廣義p值,EM演算法,廣義樞紐量,廣義信賴區間,非劣性, | zh_TW |
dc.subject.keyword | f2 factor,g1 factor,Bootstrap method,Generalized p-values,EM algorithm,Generalized pivotal quantity,Generalized confidence interval,Non-inferiority, | en |
dc.relation.page | 105 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2009-07-13 | |
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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