Using natural strata when examining unmeasured biases in an observational study of neurological side effects of antibiotics

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-02-23 DOI:10.1093/jrsssc/qlad010
K. Brumberg, Darcy E. Ellis, D. Small, S. Hennessy, P. Rosenbaum
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Abstract

Fluoroquinolones are widely prescribed antibiotics that carry a US Food and Drug Administration warning about possible side-effects on the central and peripheral nervous system. We compare 436,891 patients with sinusitis treated with fluoroquinolones to two control groups treated with azithromycin or amoxicillin. In addition to looking for nervous system complications, we look for evidence of bias using outcomes for which an effect was not anticipated. The comparison uses ‘natural strata’ that form control groups proportional in size to the treated group and balance many covariates beyond those that define the strata. The main technical contribution is a new method for near-optimal construction of natural strata with multiple groups. The online supplement material contains proofs, details, and information about the R package natstrat and replication.
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在抗生素神经系统副作用的观察性研究中,使用天然地层检查未测量偏差
氟喹诺酮类药物是一种广泛使用的抗生素,美国食品和药物管理局警告说,这种药物可能对中枢和周围神经系统产生副作用。我们比较了436,891例使用氟喹诺酮类药物治疗的鼻窦炎患者与使用阿奇霉素或阿莫西林治疗的对照组。除了寻找神经系统并发症外,我们还使用未预料到的结果寻找偏倚的证据。比较使用“自然地层”,形成与处理组成比例的对照组,并平衡定义地层之外的许多协变量。主要的技术贡献是提出了一种多群天然地层近最优施工的新方法。在线补充材料包含证据、细节和有关R包的natstrat和复制的信息。
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
76
审稿时长
>12 weeks
期刊介绍: The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
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