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The performance of sequence symmetry analysis as a tool for post-market surveillance of newly marketed medicines: a simulation study

机译:序列对称性分析作为新上市药品上市后监测工具的性能:模拟研究

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Background Sequence symmetry analysis (SSA) is a potential tool for rapid detection of adverse drug events (ADRs) associated with newly marketed medicines utilizing computerized claims data. SSA is robust to patient specific confounders but it is sensitive to the underlying utilization trends in the medicines of interest. Methods to adjust for utilisation trends have been developed, however, there has been no systematic investigation to assess the performance of SSA when variable prescribing trends occur. The objective of this study was to evaluate the validity of SSA as a signal detection tool for newly marketed medicines. Methods Randomly simulated prescription supplies for a population of 1 million were generated for two medicines, DrugA (medicine of interest) and DrugB (medicine indicative of an adverse event). Scenarios were created by varying medicine utilization trends for a newly marketed medicine (DrugA). In addition, the magnitude of association between DrugA and DrugB was varied. For each scenario 1000 simulations were generated. Average Adjusted Sequence Ratios (ASR), bootstrapped 95% confidence intervals (CIs), percentage of CI's which covered the expected ASR and percent relative bias were calculated. Results When no association was simulated between DrugA and DrugB, over 95% of SSA CI's covered the expected ASR (ASR?=?1) and relative bias was 1% or less irrespective of medicine utilization trends. In scenarios where DrugA and DrugB were associated (ASR?=?2), unadjusted SR's were underestimated by between 11.7 and 15.3%. After adjustment for trend, ASR estimates were close to expected with relative bias less than 1%. Power was over 80% in all scenarios except for one scenario in which medicine uptake was gradual and the effect of interest was weak (ASR?=?1.2). Conclusions Adjustment for underlying medicine utilization patterns effectively overcomes potential under-ascertainment bias in SSA analyses. SSA may be effectively applied as a safety signal detection tool for newly marketed medicines where sufficiently large health claim data are available.
机译:背景序列对称性分析(SSA)是一种潜在工具,可利用计算机化的理赔数据快速检测与新上市药物相关的不良药物事件(ADR)。 SSA对特定于患者的混杂因素具有鲁棒性,但对目标药物的潜在利用趋势敏感。已经开发出适应利用趋势的方法,但是,当发生可变处方趋势时,没有系统的研究来评估SSA的性能。这项研究的目的是评估SSA作为新上市药物的信号检测工具的有效性。方法针对两种药物,即DrugA(目标药物)和DrugB(指示不良事件的药物),生成了针对100万人口的随机模拟处方药。通过改变新上市药物(DrugA)的药物利用趋势来创建方案。另外,DrugA和DrugB之间的关联程度也有所不同。对于每个场景,生成了1000个模拟。计算平均调整序列比(ASR),自举95%置信区间(CI),覆盖预期ASR的CI百分比和相对偏差百分比。结果当在DrugA和DrugB之间没有模拟关联时,超过95%的SSA CI涵盖了预期的ASR(ASR?=?1),相对偏倚为1%或更低,与药物使用趋势无关。在将DrugA和DrugB相关联的情况下(ASR?=?2),未经调整的SR被低估了11.7%至15.3%。调整趋势后,ASR估计值接近预期,相对偏差小于1%。在所有情况下,功率均超过80%,除了一种情况下,这种情况是渐进的药物吸收且兴趣影响较弱(ASR?=?1.2)。结论调整基本药物使用模式可以有效克服SSA分析中潜在的不确定性偏倚。如果有足够大的健康声明数据,SSA可以有效地用作新上市药品的安全信号检测工具。

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