請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/32210
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 高純琇 | |
dc.contributor.author | Chun-Hui Su | en |
dc.contributor.author | 蘇純慧 | zh_TW |
dc.date.accessioned | 2021-06-13T03:36:54Z | - |
dc.date.available | 2007-08-03 | |
dc.date.copyright | 2006-08-03 | |
dc.date.issued | 2006 | |
dc.date.submitted | 2006-07-26 | |
dc.identifier.citation | 1. Slone D, Shapiro S, Miettinen OS, Finkle WD, Stolley PD. Drug evaluation after marketing. Ann Intern Med 1979;90(2):257-61.
2. International Drug Monitoring: The Role of National Centres. Geneva: World Health Organization; 1972. 3. Taussig HB. A study of the German outbreak of phocomelia. The thalidomide syndrome. JAMA 1962;180:1106-14. 4. Klepper MJ. The periodic safety update report as a pharmacovigilance tool. Drug Saf 2004;27(8):569-78. 5. 尤耀德, 高純琇. 國際上藥品在評估制度與風險管理機制介紹. Drug safety newsletter 2004:20-8. 6. Clark JA, Klincewicz SL, Stang PE. Overview-spontaneous signalling. In: Mann RD, Andrewa EB, eds. Pharmacovigilance. John Wiley & Sons Press; 2002:247-71. 7. 全國藥物不良反應通報系統設置宗旨與沿革 http://adr.doh.gov.tw/default.asp. 2006. [Accessed 2006 4/12.] 8. 尤耀德, 高純琇. 參加國際藥物流行病學學會第二十一屆國際學術研討會心得. Drug safety newslwtter 2004;5:19-24. 9. Egberts AC, Meyboom RH, van Puijenbroek EP. Use of measures of disproportionality in pharmacovigilance: three Dutch examples. Drug Saf 2002;25(6):453-8. 10. Meyboom RH, Egberts AC, Edwards IR, Hekster YA, de Koning FH, Gribnau FW. Principles of signal detection in pharmacovigilance. Drug Saf 1997;16(6):355-65. 11. Hauben M, Madigan D, Gerrits CM, Walsh L, Van Puijenbroek EP. The role of data mining in pharmacovigilance. Expert Opin Drug Saf 2005;4(5):929-48. 12. Chan KA, Hauben M. Signal detection in pharmacovigilance: empirical evaluation of data mining tools. Pharmacoepidemiol Drug Saf 2005;14(9):597-9. 13. Hauben M, Reich L. Potential utility of data-mining algorithms for early detection of potentially fatal/disabling adverse drug reactions: a retrospective evaluation. J clin pharmaco 2005;45:378-84. 14. Hauben M, Patadia V, Gerrits C, Walsh L, Reich L. Data mining in pharmacovigilance: the need for a balanced perspective. Drug Saf 2005;28(10):835-42. 15. van Puijenbroek E, Diemont W, van Grootheest K. Application of quantitative signal detection in the Dutch spontaneous reporting system for adverse drug reactions. Drug Saf 2003;26(5):293-301. 16. Routledge P. 150 years of pharmacovigilance. Lancet 1998;351:1200-1. 17. Hanley JA, Lippman-Hand A. If nothing goes wrong, is everything all right? Interpreting zero numerators. JAMA 1983;249(13):1743-5. 18. Evans AS. Causation and disease: the Henle-Koch postulates revisited. Yale J Biol Med 1976;49(2):175-95. 19. Hauben M. A brief primer on automated signal detection. Ann Pharmacother 2003;37(7-8):1117-23. 20. Szarfman A, Tonning JM, Doraiswamy PM. Pharmacovigilance in the 21st century: new systematic tools for an old problem. Pharmacotherapy 2004;24(9):1099-104. 21. Graham DJ, Ahmad SR, Pizza-hepp T. Spontaneous reporting-USA. In: Mann RD, Andrewa EB, eds. Pharmacovigilance. John Wiley & Sons Press; 2002:219-27. 22. Evans SJ, Waller PC, Davis S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiol Drug Saf 2001;10(6):483-6. 23. Davids S, Raine JM. Spontaneous reporting-UK. In: Mann RD, Andrewa EB, eds. Pharmacovigilance. John Wiley & Sons Press; 2002:195-207. 24. Waller PC, Bahri P. Regulatory pharmacovigilance in the EU. In: Mann RD, Andrewa EB, eds. Pharmacovigilance. John Wiley & Sons Press; 2002:183-94. 25. Almenoff J, Tonning JM, Gould AL, et al. Perspectives on the use of data mining in pharmaco-vigilance. Drug Saf 2005;28(11):981-1007. 26. WHO Collaborating Centre for International Drug Monitoring, National Pharmacovigilance System. 1997. 27. Bégaud B MY, Haramburu F. Quality improvement and statistical calculations made on spontaneous reports. Drug Inf J 1994;28:1187-95. 28. 全國藥物不良反應通報系統設置宗旨與沿革 http://adr.doh.gov.tw/default.asp. [Accessed 2006 4/12.] 29. Brown EG, Wood L, Wood S. The medical dictionary for regulatory activities (MedDRA). Drug Saf 1999;20(2):109-17. 30. MedDRA 網站 http://www.meddramsso.com/MSSOWeb/index.htm. [Accessed 2006 4/14.] 31. Coding Plus TThe Expert MedDRA Solution ProviderSM http://www.codingplus.com/focus.htm. [Accessed 2006 4/14.] 32. Kubota K. Prescription-event monitoring in Japan (J-PEM). Drug Saf 2002;25(6):441-4. 33. van Puijenbroek EP, Bate A, Leufkens HG, Lindquist M, Orre R, Egberts AC. A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiol Drug Saf 2002;11(1):3-10. 34. Bate A, Lindquist M, Orre R, Edwards IR, Meyboom RH. Data-mining analyses of pharmacovigilance signals in relation to relevant comparison drugs. Eur J Clin Pharmacol 2002;58(7):483-90. 35. Edwards IR, Biriell C. Harmonisation in pharmacovigilance. Drug Saf 1994;10(2):93-102. 36. Hauben M, Reich L. Safety related drug-labelling changes: findings from two data mining algorithms. Drug Saf 2004;27(10):735-44. 37. Amery WK. Signal generation from spontaneous adverse event reports. Pharmacoepidemiol Drug Saf 1999;8(2):147-50. 38. Hauben M, Reich L, Chung S. Postmarketing surveillance of potentially fatal reactions to oncology drugs: potential utility of two signal-detection algorithms. Eur J Clin Pharmacol 2004;60(10):747-50. 39. Meyboom RH, Lindquist M, Egberts AC, Edwards IR. Signal selection and follow-up in pharmacovigilance. Drug Saf 2002;25(6):459-65. 40. Finney DJ. Statisical logic in the monitoring of reactions to therapeutic drugs. Methods Inf Med 1971;10(4):237-45. 41. Finney DJ. Systemic signalling of adverse reactions to drugs. Methods Inf Med 1974;13(1):1-10. 42. Finney DJ. The detection of adverse reactions to therapeutic drugs. Stat Med 1982;1(2):153-61. 43. Hauben M, Zhou X. Quantitative methods in pharmacovigilance: focus on signal detection. Drug Saf 2003;26(3):159-86. 44. Banks D, Woo EJ, Burwen DR, Perucci P, Braun MM, Ball R. Comparing data mining methods on the VAERS database. Pharmacoepidemiol Drug Saf 2005;14(9):601-9. 45. Evans SJ. Pharmacovigilance: a science or fielding emergencies? Stat Med 2000;19(23):3199-209. 46. Evans S. Statistical methods of signal detection. In: Mann RD, Andrewa EB, eds. Pharmacovigilance. John Wiley & Sons Press; 2002:273-9. 47. Moore N, Hall G, Sturkenboom M, Mann R, Lagnaoui R, Begaud B. Biases affecting the proportional reporting ratio (PPR) in spontaneous reports pharmacovigilance databases: the example of sertindole. Pharmacoepidemiol Drug Saf 2003;12(4):271-81. 48. Rothman KJ, Lanes S, Sacks ST. The reporting odds ratio and its advantages over the proportional reporting ratio. Pharmacoepidemiol Drug Saf 2004;13(8):519-23. 49. Gould AL. Practical pharmacovigilance analysis strategies. Pharmacoepidemiol Drug Saf 2003;12(7):559-74. 50. Arlett P, Moseley J, Seligman PJ. A view from regulatory agencies. In: strom BL, ed. Pharmacoepidemiology. 4 ed. New York: John Wiley & Sons Ltd.; 2005:103-30. 51. Moore N, Kreft-Jais C, Haramburu F, et al. Reports of hypoglycaemia associated with the use of ACE inhibitors and other drugs: a case/non-case study in the French pharmacovigilance system database. Br J Clin Pharmacol 1997;44(5):513-8. 52. van Puijenbroek EP, van Grootheest K, Diemont WL, Leufkens HG, Egberts AC. Determinants of signal selection in a spontaneous reporting system for adverse drug reactions. Br J Clin Pharmacol 2001;52(5):579-86. 53. van Puijenbroek EP, Egberts AC, Heerdink ER, Leufkens HG. Detecting drug-drug interactions using a database for spontaneous adverse drug reactions: an example with diuretics and non-steroidal anti-inflammatory drugs. Eur J Clin Pharmacol 2000;56(9-10):733-8. 54. Van Puijenbroek EP, Egberts AC, Meyboom RH, Leufkens HG. Signalling possible drug-drug interactions in a spontaneous reporting system: delay of withdrawal bleeding during concomitant use of oral contraceptives and itraconazole. Br J Clin Pharmacol 1999;47(6):689-93. 55. van Puijenbroek EP, Egberts AC, Meyboom RH, Leufkens HG. Association between terbinafine and arthralgia, fever and urticaria: symptoms or syndrome? Pharmacoepidemiol Drug Saf 2001;10(2):135-42. 56. Szarfman A, Machado SG, O'Neill RT. Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA's spontaneous reports database. Drug Saf 2002;25(6):381-92. 57. Bate A, Lindquist M, Edwards IR, Orre R. A data mining approach for signal detection and analysis. Drug Saf 2002;25(6):393-7. 58. Bate A, Lindquist M, Edwards IR, et al. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol 1998;54(4):315-21. 59. ATC/DDD Index 2006 http://www.whocc.no/atcddd/. [Accessed 2006 4.] 60. Wodtke JM, Generali JA. Use of medical record codes to identify adverse drug reactions. Am J Hosp Pharm 1993;50(9):1915-6. 61. Rawlins M, Thompson J. Pathogenesis of adverse drug reactions. Oxford: Oxford university press; 1977. 62. Thiessard F, Roux E, Miremont-Salame G, et al. Trends in spontaneous adverse drug reaction reports to the French pharmacovigilance system (1986-2001). Drug Saf 2005;28(8):731-40. 63. Mittmann N, Knowles SR, Gomez M, Fish JS, Cartotto R, Shear NH. Evaluation of the extent of under-reporting of serious adverse drug reactions: the case of toxic epidermal necrolysis. Drug Saf 2004;27(7):477-87. 64. Backstrom M, Mjorndal T, Dahlqvist R. Under-reporting of serious adverse drug reactions in Sweden. Pharmacoepidemiol Drug Saf 2004;13(7):483-7. 65. Sweis D, Wong IC. A survey on factors that could affect adverse drug reaction reporting according to hospital pharmacists in Great Britain. Drug Saf 2000;23(2):165-72. 66. Alvarez-Requejo A, Carvajal A, Begaud B, Moride Y, Vega T, Arias LH. Under-reporting of adverse drug reactions. Estimate based on a spontaneous reporting scheme and a sentinel system. Eur J Clin Pharmacol 1998;54(6):483-8. 67. Lacoste-Roussillon C, Pouyanne P, Haramburu F, Miremont G, Begaud B. Incidence of serious adverse drug reactions in general practice: a prospective study. Clin Pharmacol Ther 2001;69(6):458-62. 68. Pouyanne P, Haramburu F, Imbs JL, Begaud B. Admissions to hospital caused by adverse drug reactions: cross sectional incidence study. French Pharmacovigilance Centres. Bmj 2000;320(7241):1036. 69. Moride Y, Haramburu F, Requejo AA, Begaud B. Under-reporting of adverse drug reactions in general practice. Br J Clin Pharmacol 1997;43(2):177-81. 70. Center for Drug Evaulation and Research 2004 report to The nation-Improving Public health through human drugs. http://www.fda.gov/cder/reports/rtn/2004/rtn2004.htm. [Accessed 2006 5/29.] 71. 藥事法. In: 衛生署藥政處 http://www.doh.gov.tw/ufile/doc/930421新修藥事法--全文版--930517校正.doc; 2004:9. 72. van Grootheest K, Olsson S, Couper M, de Jong-van den Berg L. Pharmacists' role in reporting adverse drug reactions in an international perspective. Pharmacoepidemiol Drug Saf 2004;13(7):457-64. 73. Begaud B, Martin K, Fourrier A, Haramburu F. Does age increase the risk of adverse drug reactions? Br J Clin Pharmacol 2002;54(5):550-2. 74. Ogilvie RI, Ruedy J. Adverse drug reactions during hospitalization. Can Med Assoc J 1967;97(24):1450-7. 75. Micromedex(R) healthcare Series[computer program] volume 125. [Accessed 2006/6] 76. Roujeau JC, Kelly JP, Naldi L, et al. Medication use and the risk of Stevens-Johnson syndrome or toxic epidermal necrolysis. N Engl J Med 1995;333(24):1600-7. 77. Fritsch PO, Sidoroff A. Drug-induced Stevens-Johnson syndrome/toxic epidermal necrolysis. Am J Clin Dermatol 2000;1(6):349-60. 78. Letko E, Papaliodis DN, Papaliodis GN, Daoud YJ, Ahmed AR, Foster CS. Stevens-Johnson syndrome and toxic epidermal necrolysis: a review of the literature. Ann Allergy Asthma Immunol 2005;94(4):419-36; quiz 36-8, 56. 79. Martindale:The complete drug reference [From MicroMedex(R)]. London: Pharmaceutical Press, 2005, [Accessed 2006 6/7.] 80. American Hospital Formulary Service drug information (AHFS DI). In: Bethesda, MD: the Board of Directors of the American Society of Hospital Pharmacists, 1984-1988; AHFS. 81. Bulpitt CJ, Beckett NS, Fletcher AE, et al. Withdrawal from treatment in the Syst-Eur Trial. J Hypertens 2002;20(2):339-46. 82. Edwards C, Blowers DA, Pover GM. Fosinopril national survey: a post-marketing surveillance study of fosinopril (Staril) in general practice in the UK. Int J Clin Pract 1997;51(6):394-8. 83. Rosenthal J, Bahrmann H, Benkert K, et al. Analysis of adverse effects among patients with essential hypertension receiving an ACE inhibitor or a beta-blocker. Cardiology 1996;87(5):409-14. 84. Manzato E, Capurso A, Crepaldi G. Modification of cardiovascular risk factors during antihypertensive treatment: a multicentre trial with quinapril. J Int Med Res 1993;21(1):15-25. 85. Knapp LE, Frank GJ, McLain R, Rieger MM, Posvar E, Singer R. The safety and tolerability of quinapril. J Cardiovasc Pharmacol 1990;15 Suppl 2:S47-55. 86. Pfeffer MA, McMurray JJ, Velazquez EJ, et al. Valsartan, captopril, or both in myocardial infarction complicated by heart failure, left ventricular dysfunction, or both. N Engl J Med 2003;349(20):1893-906. 87. Roca-Cusachs A, Oigman W, Lepe L, Cifkova R, Karpov YA, Harron DW. A randomized, double-blind comparison of the antihypertensive efficacy and safety of once-daily losartan compared to twice-daily captopril in mild to moderate essential hypertension. Acta Cardiol 1997;52(6):495-506. 88. Black HR, Graff A, Shute D, et al. Valsartan, a new angiotensin II antagonist for the treatment of essential hypertension: efficacy, tolerability and safety compared to an angiotensin-converting enzyme inhibitor, lisinopril. J Hum Hypertens 1997;11(8):483-9. 89. Kaplan NM. The CARE Study: a postmarketing evaluation of ramipril in 11,100 patients. The Clinical Altace Real-World Efficacy (CARE) Investigators. Clin Ther 1996;18(4):658-70. 90. Law M, Rudnicka AR. Statin safety: a systematic review. Am J Cardiol 2006;97(8A):52C-60C. 91. Chang JT, Staffa JA, Parks M, Green L. Rhabdomyolysis with HMG-CoA reductase inhibitors and gemfibrozil combination therapy. Pharmacoepidemiol Drug Saf 2004;13(7):417-26. 92. Physicians' desk reference [From MicroMedex(R)]. Oradell, N.J.: Medical Economics Co., [Accessed 2006/6/14.] 93. Remington G, Kapur S. D2 and 5-HT2 receptor effects of antipsychotics: bridging basic and clinical findings using PET. J Clin Psychiatry 1999;60 Suppl 10:15-9. 94. Pierre JM. Extrapyramidal symptoms with atypical antipsychotics: incidence, prevention and management. Drug Saf 2005;28(3):191-208. 95. Morcos SK. Review article: Acute serious and fatal reactions to contrast media: our current understanding. Br J Radiol 2005;78(932):686-93. 96. Thomsen HS, Bush WH, Jr. Adverse effects of contrast media: incidence, prevention and management. Drug Saf 1998;19(4):313-24. 97. DuMouchel W. Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system. The American Statistician 1999;53(3):177-90. 98. DuMouchel W, Pregibon D. Empirical Bayes screening for multi-item associations. In:Proceeding of the Seventh ACM SIGKDD International Conference on Knowledge discovery in Data. 2001:67-76. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/32210 | - |
dc.description.abstract | 研究目的
藥物不良反應之主動通報系統的建立,是上市後藥品安全監視的主要方法,目的是在短時間內發現上市前未知的藥物不良反應或已知藥物不良反應的發生率的改變。此篇論文,應用文獻中已建構的兩種簡易資料探勘法在臺灣的藥物不良反應資料庫,進行訊號偵測。並且比較兩種方法的可行性。 研究方法 收集全國藥物不良反應通報系統中九十二至九十四年度的通報案例,所有案例經由評估和譯碼。可疑藥品以Anatomical Therapeutic Chemical (ATC) code做為編碼,不良反應以Medical Dictionary for Medical regulatory Activities (MedDRA) 的Preferred term (PT) code做為編碼。將由各通報案例產生出的ATC-PT配對,以相對通報比率法 (proportional reporting ratios, PRRs) 與相對通報勝算比 (reporting odds ratio, RORs)進行訊號偵測。 研究結果 92-94年三年度納入分析的通報案例共8439件。通報案件的病患,男女比例為0.97:1,平均年齡為52.1±21.7歲,以70至80歲最多(17.2%)。通報案件的懷疑藥品之藥理分類,以神經系統作用藥物最常見(30.3%),抗感染藥物次之(27.2%),心血管腎臟用藥佔第三(11.9%)。根據MedDRA的System Organ Class (SOCs),最常被通報的不良反應是皮膚與皮下組織方面的疾病(32.2%),神經系統方面的異常次之(10.5%),胃腸道的疾病為第三(10.0%)。不良反應的型態,以B型態為主(67%)。在藥物與不良反應相關性的評估方面,「確定」、「極有可能」與「可能」者,共佔91.3%。 三個年度的通報案例總計配對出14,072筆,共7,264種不同的ATC -PT配對,其中有974種配對(13.4%)的出現次數大於等於3。此974種配對經過訊號偵測後,由PRR法偵測出的訊號有648個配對(8.9%),由ROR法偵測出的訊號有722個配對(9.9%)。其中有647個配對同時可以被此兩種方法偵測到,251組配對經PRR法與ROR法運算皆未被視為訊號。在進行第二階相對於同類藥品的訊號偵測,則同時可被PRR法及ROR法偵測到的訊號有244個配對(3.4%)。 討論 在647個同時可以被兩種方法偵測到的配對中,有17個配對(2.6%)是在文獻中,沒有明確記載其藥物不良反應相關性者,需要進一步研究確定藥物不良反應相關性。另外關於statin類與fibrate類降血脂藥物造成橫紋肌溶解症、顯影劑造成過敏性休克等藥物不良反應也應列入後續追蹤的項目之一。 結論 目前電腦化的訊號偵測資料探勘法,並無單一的閾值設定標準。使用PRR法及ROR法做為臺灣藥物不良反應資料庫的訊號偵測法,可能已經足夠。訊號偵測法可以當作為傳統藥物偵測法很好的輔助工具,資料探勘法產生的訊號,仍需要進一步的文獻回顧與研究,以確定其藥物不良反應的相關性與臨床意義,以建立用藥安全資訊。 | zh_TW |
dc.description.abstract | Purpose
Spontaneous reporting systems (SRS) for adverse drug reactions (ADRs) remain a cornerstone of the Pharmacovigilance to early detect new ADRs and changes of the frequency of ADRs that are already known. In this study, we aimed to apply two simple data-mining algorithms developed in literature to the safety signal detection for the ADR database built in Taiwan. The feasibility of the two different methods was compared as well. Methods Reported cases collecting from 2003 to 2005 in the database of National reporting System of Adverse Drug Reactions in Taiwan were reviewed and coded with ATC code for suspected drugs and with MedDRA code for the reported adverse drug reactions. Preferred term (PT) of the MedDRA coding was used to represent the ADR. The priciples of proportional reporting ratio (PRR) and reporting odds ratio (ROR) were applied to ATC-PT pairs generated from the reports for signal detection. Results A total of 8439 reports over the 3-year period were included. The male/female ratio of patients was 0.97:1 and the average age of patients was 52.1±21.7 years old. The largest patients group was in the age range of 70-80 yrs (17.2%). The most frequently reported suspected drugs were the drugs used in nervous system (30.3%), followed by antiinfectives (27.2%), and cardiovascular- renal drugs (22.9%). According to MedDRA’s system organ classification, the most often reported ADRs were skin and subscutaneous tissue disorders (32.2%), followed by nervous system disorders (10.5%) and gastrointestinal disorders (10.0%). Type B ADRs are the main type of ADRs (67%). The causality of suspected drugs and ADRs was assessed as certain, probable, and possible for 91.3% of the cases. A total of 14,072 ATC-PT pairs were generated from the reported cases, which belong to 7,364 different ATC-PT pairs. Among them, 974 (13.4%) ATC-PT pairs have the frequencies at least 3. After calculation, 648 signals (8.9%) were generated by PRR method and 722 signals (9.9%) were from ROR method. It is interesting to see that 647 different ATC-PT pairs (8.9%) were detected by both methods. Only 251 different pairs were not considered as signal by either method. If we took the 723 ATC-PT pairs to do the calculations with respect to the same drug class (based on ATC classification), a total of 244 signals (3.4%) were detected by both PRR and ROR methods. Discussion After searching, the relationship between suspected drug and ADR for 17 ATC-PT pairs (2.6% out of the 647 pairs) was not found in literatures. They may be a good candidate for further investigation. Moreover, antihyperlipidemia drug such as statins and fibrates related rhabdomyolysis and contrast medias related anaphylaxic shock are recommended for further carefully monitoring. Conclusion There is no single standard for signal detection method so far. The PRR method and ROR method may be good enough for safety signal detection for the ADR database of Taiwan. However, statistic approach signal detection method should be considered as a potential supplement to, and not substitute for, traditional pharmacovigilance strategies. Further clinical/pharmacological knowledge- based evaluation and research are required to confirm the casuality between the drug and the adverse drug reaction. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T03:36:54Z (GMT). No. of bitstreams: 1 ntu-95-R93451003-1.pdf: 909389 bytes, checksum: 6547737fcdb7504ebf7205973368ce34 (MD5) Previous issue date: 2006 | en |
dc.description.tableofcontents | 目錄
中文摘要 I 英文摘要 III 縮寫或簡稱對照表VI 中英文名詞對照表VII 表目錄XI 圖目錄XIV 第一章 前言 1 第一節 研究背景 1 第二節 研究動機 3 第二章 文獻探討 4 第一節 藥品安全監視制度 4 一. 歷史 4 二. 國際上的藥品監視系統 7 三. 台灣 9 第二節 不良反應譯碼系統 11 第三節 藥物不良反應訊號偵測 14 一. 訊號偵測概論 14 二. 相對通報比率法 17 三. 相對通報勝算比 21 四. 貝氏法 23 第三章 研究目的 31 第四章 研究材料與工具 32 第一節 台灣藥物不良反應資料庫 32 第二節 藥物與不良反應的譯碼 34 第三節 描述性統計之變項定義 35 第四節 簡易訊號偵測法 39 一. 初階訊號偵測 39 二. 第二階段訊號偵測 40 第五節 資料處理及分析 42 第五章 研究結果 43 第一節 通報案件描述性分析 43 第二節 ATC-PT 配對的描述性分析 55 第三節 訊號偵測 63 一. 初階之訊號偵測 63 二. 第二階之訊號偵測 64 第六章 討論 104 第一節 通報案件描述性分析 104 第二節 不良反應的描述性分析 107 第三節 偵測所得訊號的臨床意義 110 第四節 訊號偵測未解決的問題和挑戰 120 第五節 訊號偵測的限制 122 第六節 訊號的後續追蹤 123 第七章 結論 125 第七章 結論 125 參考文獻 126 附錄 136 | |
dc.language.iso | zh-TW | |
dc.title | 建立藥物不良反應的訊號探測方法
以全國藥物不良反應資料庫為研究資料 | zh_TW |
dc.title | The Study of Signal Detection from National Reporting System of Adverse Drug Reaction Database in Taiwan | en |
dc.type | Thesis | |
dc.date.schoolyear | 94-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林慧玲,高雅慧 | |
dc.subject.keyword | 藥物不良反應,藥物不良反應資料庫訊號偵測,資料探勘, | zh_TW |
dc.subject.keyword | Adverse drug reaction,ADR database,signal detection,data mining, | en |
dc.relation.page | 148 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2006-07-27 | |
dc.contributor.author-college | 醫學院 | zh_TW |
dc.contributor.author-dept | 臨床藥學研究所 | zh_TW |
顯示於系所單位: | 臨床藥學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-95-1.pdf 目前未授權公開取用 | 888.08 kB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。