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对累积剂量和暴露持续时间进行建模,为了解苯二氮䓬类药物与损伤之间的关联提供了见解。

Modeling cumulative dose and exposure duration provided insights regarding the associations between benzodiazepines and injuries.

作者信息

Abrahamowicz Michal, Bartlett Gillian, Tamblyn Robyn, du Berger Roxane

机构信息

Department of Epidemiology and Biostatistics, McGill University, Montreal, Québec, Canada.

出版信息

J Clin Epidemiol. 2006 Apr;59(4):393-403. doi: 10.1016/j.jclinepi.2005.01.021.

Abstract

BACKGROUND AND OBJECTIVES

Accurate assessment of medication impact requires modeling cumulative effects of exposure duration and dose; however, postmarketing studies usually represent medication exposure by baseline or current use only. We propose new methods for modeling various aspects of medication use history and employment of them to assess the adverse effects of selected benzodiazepines.

STUDY DESIGN AND SETTING

Time-dependent measures of cumulative dose or duration of use, with weighting of past exposures by recency, were proposed. These measures were then included in alternative versions of the multivariable Cox model to analyze the risk of fall related injuries among the elderly new users of three benzodiazepines (nitrazepam, temazepam, and flurazepam) in Quebec. Akaike's information criterion (AIC) was used to select the most predictive model for a given benzodiazepine.

RESULTS

The best-fitting model included a combination of cumulative duration and current dose for temazepam, and cumulative dose for flurazepam and nitrazepam, with different weighting functions. The window of clinically relevant exposure was shorter for flurazepam than for the two other products.

CONCLUSION

Careful modeling of the medication exposure history may enhance our understanding of the mechanisms underlying their adverse effects.

摘要

背景与目的

准确评估药物影响需要对暴露持续时间和剂量的累积效应进行建模;然而,上市后研究通常仅通过基线使用情况或当前使用情况来表示药物暴露。我们提出了对用药史的各个方面进行建模的新方法,并运用这些方法来评估某些苯二氮䓬类药物的不良反应。

研究设计与环境

我们提出了随时间变化的累积剂量或使用持续时间的测量方法,并根据近期程度对过去的暴露情况进行加权。然后,将这些测量方法纳入多变量Cox模型的不同版本中,以分析魁北克省三种苯二氮䓬类药物(硝西泮、替马西泮和氟西泮)老年新使用者中与跌倒相关伤害的风险。使用赤池信息准则(AIC)为给定的苯二氮䓬类药物选择最具预测性的模型。

结果

拟合效果最佳的模型包括替马西泮的累积持续时间和当前剂量的组合,以及氟西泮和硝西泮的累积剂量,并采用了不同的加权函数。氟西泮的临床相关暴露窗口比其他两种产品短。

结论

对用药暴露史进行仔细建模可能会增强我们对其不良反应潜在机制的理解。

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