Methods of Controlling Confounding Factors in Adverse Drug Reaction Signal Detection

Abstract

Abstract: It is impossible to randomize the allocation of people using the drug under the post-marketing condition, so hard to control the confounding bias in signal detection of adverse drug reaction(ADR) databases. This article briefly introduces three widely-used Methods of controlling confounding bias. It give a brief description of the advantages and limitations of each method, and their applications in ADR signal detection in order to provide some effective information for the post-marketing surveillance.

Key words: signal detection, confounding, stratification, logistic regression, propensity score

CLC Number:

QIAN Wei, YIE Xiao-fei, WANG Chao, HE Jia. Methods of Controlling Confounding Factors in Adverse Drug Reaction Signal Detection[J]. Chinese Journal of Pharmacovigilance, 2010, 7(3): 142-144.

References

[1] 沈福民主编．流行病学原理与方法[M].1版.上海：复旦大学出版社,上海医科大学出版社,2001：148-155.
[2] Hauben M, Bate A. Decision support methods for the detection of adverse events in post-marketing data[J]. Drug Discovery Today, 2009, 14:343-357.
[3] Almenoff J S, LaCroix K K, Yuen N A, et al. Comparative Performance of Two Quantitative Safety Signalling Methods: Implications for Use in a Pharmacovigilance Department[J]. Drug Saf, 2006, 29 (10): 875-887.
[4] Woo E J, Ball R. Burwen D R, et al. Effects of stratification on data mining in the US Vaccine Adverse Event Reporting System (VAERS)[J]. Drug Saf, 2008, 31 (8): 667-674.
[5] 罗宝章,钱轶峰,叶小飞,等.药物不良反应信号检测方法的现状与展望[J]. 药学服务与研究,2009,9(4) ：255-260.
[6] 叶小飞,王海南,陈文,等.数据挖掘在药物警戒中的应用[J]. 中国药物警戒,2008,5(1) ：36-40.
[7] Evans S J. Stratification for spontaneous report databases[J]. Drug Saf, 2008;31(11):1049-1052.
[8] Hopstadius J, Norén G N, Bate A, et al. Impact of stratification in adverse drug reaction surveillance[J]. Drug Saf, 2008, 31 (11): 1035-1048.
[9] Hauben M, Madigan D, Gerrits C M, et al. The role of data mining in pharmacovigilance[J]. Expert Opinion Drug Safe, 2005, 4(5): 929-948.
[10] Almenoff J S, Pattishall E N, Gibbs T G, et al. Novel statistical tools for monitoring the safety of marketed drugs[J]. ClinPharmacol Ther, 2007, 82 (2): 157-166.
[11] Genkin A, Lewis DD & Madigan D. BBR: Bayesian Logistic Regression Software[EB/OL].[2009-12-20] .http://www.stat.rutgers.edu/Bmadigan/BBR/ (accessed 23 April 2007).
[12] van Puijenbroek E P, Egberts A C, Heerdink E R, et al. Detecting drug-drug interactions using a database for spontaneous adverse drug reactions: an example with diuretics and non-steroidal anti-inflammatory drugs[J]. European Journal of Clinical Pharmacology, 2000, 56: 733-738.
[13] Noren G N, Sundberg R, Bate A, et al. A statistical methodology for drug drug interaction surveillance[J]. Stat Med, 2008, 27: 3057-3070.
[14] Paul R, Rubin D B. Reducing bias in observational studies using subclassification on the propensity score[J]. J Am Stat Assoc, 1984, 79: 5l6-524.
[15] 赵守军,张勇,汪萱怡,等.均衡组间差异的有效方法：倾向评分[J]. 中华流行病学杂志,2003,24(6): 516-519.
[16] 沈剑文,张亮,王大猷.倾向分析法在药物流行病学研究中的应用[J]. 药物流行病学杂志,2008,17(2): 121-125.
[17] Joffe M M, Rosenbaum P R. Invited commentary: propensity scores[J]. Am J Epidemiol, 1999, l50: 327-333.
[18] Hanley J A, Dendukuri N. Efficient sampling approaches to address confounding in database studies[J]. Statistical Methods in Medical Research, 2009,18:81-105.
[19] Rosenbaum P R, Rubin D B. Reducing bias in observational studies using subclassification on the propensity score[J]. Am Stat Assoc, 1984, 79(387): 516-524.
[20] Groenwold RHH, Hoes A W, Nichol K L, et al. Quantifying the potential role of unmeasured confounders: the example of influenza vaccination[J]. International Journal of Epidemiology, 2008, 37:1422-1429.