clinvestКачественная клиническая практикаKachestvennaya Klinicheskaya Praktika = Good Clinical Practice2588-05192618-8473ООО «Издательство ОКИ10.37489/2588-0519-2024-3-40-54QSWFWWclinvest-741Research ArticleФАРМАКОНАДЗОРPHARMACOVILIGANCEНекоторые особенности статистического анализа данных спонтанных сообщений о нежелательных лекарственных реакцияхSome features of statistical analysis of spontaneous adverse drug reporting datahttps://orcid.org/0000-0002-8436-8931БондареваИ. Б.BondarevaI. B.

Бондарева Ирина Борисовна — д.  б. н., кафедра общей и  клинической фармакологии, Медицинский институт, Медицинский факультет

Москва

Irina B. Bondareva — Dr. Sci (Biol.), Department of General and Clinical Pharmacology, Medical Institute, School of Medicine

Moscow

i_bondareva@yahoo.comhttps://orcid.org/0000-0002-6348-6867ЗыряновС. К.ZyryanovS. K.

Зырянов Сергей Кенсаринович  — д.  м. н., профессор, зав. кафедрой общей и  клинической фармакологии, Медицинский институт, Медицинский факультет; заместитель главного врача

Москва

Sergey K. Zyryanov — PhD, Dr. Sci. (Med.), Professor, Head of the Department of General and Clinical Pharmacology; Deputy Chief physician

Moscow

zyryanov-sk@rudn.ruhttps://orcid.org/0000-0002-6641-7752АсецкаяИ. Л.AsetskayaI. L.

Асецкая Ирина Львовна — к. м. н., доцент, кафедра общей и клинической фармакологии, Медицинский институт, Медицинский факультет

Москва

Irina L. Asetskaya — Cand. Sci. (Med.),Associate Professor, Department of General and Clinical Pharmacology, Medical Institute, School of Medicine

Moscow

asetskaya-il@rudn.ruhttps://orcid.org/0000-0003-1272-8042ТерёхинаЕ. Н.TerekhinaE. N.

Терёхина Елизавета Николаевна — Ординатор 1-го года кафедры общей и клинической фармакологии;  ведущий специалист

Москва

Elizaveta N. Terekhina — 1st year Resident of the Department of General and Clinical Pharmacology; Leading specialist

Moscow

1152230261@pfur.ruФГАОУ ВО «Российский университет дружбы народов имени Патриса Лумумбы»РоссияPeoples' Friendship University of Russia named after Patrice LumumbaRussian FederationФГАОУ ВО «Российский университет дружбы народов имени Патриса Лумумбы»; ГБУЗ «Городская клиническая больница №24 Департамента здравоохранения города Москвы»РоссияPeoples' Friendship University of Russia named after Patrice Lumumba; City Clinical Hospital No. 24 of the Moscow City Health DepartmentRussian FederationФГАОУ ВО «Российский университет дружбы народов имени Патриса Лумумбы»; ФГБУ «Информационно-методический центр по экспертизе, учету и анализу обращения средств медицинского применения» РосздравнадзораРоссияPeoples' Friendship University of Russia named after Patrice Lumumba; Pharmacovigilance Center of Information and Methodological Center for Expert Evaluation, Recording and Analysis of Circulation of Medical ProductsRussian Federation202430112024034054Copyright © Бондарева И.Б., Зырянов С.К., Асецкая И.Л., Терёхина Е.Н., 20242024Бондарева И.Б., Зырянов С.К., Асецкая И.Л., Терёхина Е.Н.Bondareva I.B., Zyryanov S.K., Asetskaya I.L., Terekhina E.N.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.clinvest.ru/jour/article/view/741Актуальность

Актуальность. Риск развития нежелательных лекарственных реакций представляет серьёзную проблему фармакотерапии, здравоохранения и общества в целом. Выявление «сигналов» безопасности в пострегистрационном периоде является важной задачей фармаконадзора. Системы спонтанного репортирования по-прежнему широко используются для идентификации таких сигналов на основе данных реальной клинической практики. Различные статистические методы и алгоритмы были разработаны для этих целей в рамках как частотного, так и Байесовского подхода в статистике. Статистические методы могут использоваться и для анализа факторов риска, обусловленных индивидуальными особенностями пациентов (демографические характеристики, сопутствующие заболевания и сопутствующая терапия). Выявление подгрупп пациентов с высоким риском нежелательных реакций очень важно для персонализации фармакотерапии.

Цель

Цель. Рассмотреть проблемы и особенности разработанных другими авторами и опубликованных статистических методов анализа баз данных спонтанных сообщений, что может быть полезно для корректного проведения статистического анализа и интерпретации данных пассивного фармаконадзора.

Методы

Методы. В работе мы представили известные и наиболее часто используемые частотные, или классические, методы для проведения корректного статистического анализа спонтанных сообщений. Эти методы выявления «сигнала» и их модификации для анализа влияния факторов пациента относительно просты в понимании, интерпретации и вычислении на основе таблиц сопряжённости 2x2: отношение шансов репортирования (ROR); коэффициент пропорциональности репортирования (PRR), тест на основе нормальной аппроксимации. Были обсуждены также различные подходы к проблеме множественных сравнений в рамках пассивного фармаконадзора.

Результаты

Результаты. В качестве примера упоминавшиеся выше статистические методы были применены для анализа различий в отношении фактора «пол» для репортирования нежелательной реакции «печеночная токсичность» по данным Российской базы данных фармаконадзора. Эти тесты позволили идентифицировать лекарственные препараты, для которых при анализе печёночной токсичности наблюдалась значительная диспропорциональность в отношении фактора «пол» по сравнению с другими нежелательными реакциями. Результаты всех представленных статистических методов были сопоставимы.

Выводы

Выводы. Несмотря на многочисленные потенциальные источники систематической ошибки и известные ограничения, большие базы данных спонтанных сообщений остаются широко используемым, эффективным и относительно недорогим подходом пострегистрационного фармаконадзора. С применением корректных статистических методов базы данных спонтанных сообщений представляют собой ценный источник информации для формулирования гипотез, а также для выявления факторов риска и популяций риска.

Introduction

Introduction. Risk of adverse drug reactions (ADRs) is a serious issue in pharmacotherapy and a major public health concern. Safety signal detection during the post-marketing phase is one of the most important goals of drug safety surveillance. Spontaneous reporting systems (SRS) are still widely used to identify safety signals based on real-world data. Various data mining statistical methods have been developed for this purpose, and they are classified into frequentist and Bayesian approaches. Statistical methods can also be used for the analysis of patient-related risk factors (demographic characteristics, concomitant diseases or medications). Identification of patients at high ADR risk is important for personalized pharmacotherapy.

Objective

Objective. To present and review issues and features of the statistical methods for SRS data, developed by other authors and published in the literature, this tool may be useful for appropriate statistical analysis and accurate interpretation of passive surveillance data.

Methods

Methods. In this paper, we present the known and commonly used frequentist or classical methods for correct statistical analysis of spontaneous reports. These methods for signal detection and their modification for drug-host factor interaction analysis are relatively easy to understand, interpret, and compute based on the contingency 2x2 tables: reporting odds ratio (ROR), proportional reporting ratio (PRR), and normal approximation test. Different approaches to the multiple comparison problem in passive safety surveillance settings were also discussed.

Results

Results. As an example, the aforementioned methods were applied to analyze sex disparities in liver toxicity based on the spontaneous reports extracted from the Russian National Pharmacovigilance database. The tests identified drugs for which liver toxicity demonstrates significant disproportionality regarding sex compared with other AEs. The results of all statistical methods were similar.

Conclusions

Conclusions. Although spontaneous report databases are subject to numerous potential sources of bias and well-known limitations, these large-scale databases remain a widely used, effective, and relatively inexpensive approach for post-marketed drug surveillance. With the use of correct statistical methods, spontaneous reporting databases can provide valuable information for hypothesis generation, which should be investigated further, as well as essential data on the evaluation of risk factors and risk populations.

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The authors declare that there are no conflicts of interest present.