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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">nid</journal-id><journal-title-group><journal-title xml:lang="ru">Нефрология и диализ</journal-title><trans-title-group xml:lang="en"><trans-title>Nephrology and Dialysis</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1680-4422</issn><issn pub-type="epub">2618-9801</issn><publisher><publisher-name>Российское диализное общество</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.28996/2618-9801-2019-4-430-441</article-id><article-id custom-type="elpub" pub-id-type="custom">nid-308</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ШКОЛА НЕФРОЛОГА</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>EDUCATIONAL MATERIALS</subject></subj-group></article-categories><title-group><article-title>«Подводные камни» статистического анализа и клинической интерпретации полученных оценок на примере пациентов с хронической болезнью почек. Часть II: Анализ выживаемости</article-title><trans-title-group xml:lang="en"><trans-title>Pitfalls of statistical analysis and clinical interpretation of the estimates of patients with chronic kidney disease. Part II: Survival analysis</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Зулькарнаев</surname><given-names>А. Б.</given-names></name><name name-style="western" xml:lang="en"><surname>Zulkarnaev</surname><given-names>A. B.</given-names></name></name-alternatives><email xlink:type="simple">7059899@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Хирургическое отделение трансплантологии и диализа, ГБУЗ МО Московский областной научно-исследовательский клинический институт им. М.Ф. Владимирского</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Surgical Department of Transplantology and dialysis, M.F. Vladimirsky Moscow Regional Research Clinical Institute</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2019</year></pub-date><pub-date pub-type="epub"><day>07</day><month>08</month><year>2024</year></pub-date><volume>21</volume><issue>4</issue><fpage>430</fpage><lpage>441</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Зулькарнаев А.Б., 2024</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="ru">Зулькарнаев А.Б.</copyright-holder><copyright-holder xml:lang="en">Zulkarnaev A.B.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://journal.nephro.ru/jour/article/view/308">https://journal.nephro.ru/jour/article/view/308</self-uri><abstract><p>Анализ выживаемости - один из самых распространенных методов статистического анализа. При мнимой простоте этот анализ имеет определенные подводные камни. Существуют разные подходы, каждый из которых требует соблюдения ряда условий и своеобразной клинической интерпретации. Риск смерти можно проанализировать при помощи непосредственного измерения относительного риска или косвенной его оценке при помощи отношения шансов. Однако эти показатели носят кумулятивный характер, не предполагают цензурированных наблюдений, не учитывают время наблюдения и влияние ковариат. Наиболее распространенными методами анализа в условиях цензурированных наблюдений являются процедуры Каплан-Мейера (которая позволяет эмпирически оценить вероятность пережить определенное время - функцию выживаемости) и Нельсона-Алена (которая позволяет оценить функцию кумулятивного риска). Оба этих метода не требуют априорной информации о виде функции выживаемости, однако они позволяют оценить влияние на выживаемость (или риск) только одного категориального предиктора, не имеют возможности провести коррекцию на ковариаты и основаны на предположении о неинформативном цензурировании. Использование этих методов в условиях конкурирующих рисков дает заведомо предвзятую оценку выживаемости. Наиболее распространенным методом анализа выживаемости при наличии конкурирующих рисков является причинно-специфическая модель пропорциональных рисков Кокса. Использование этого метода целесообразно, когда целью исследователя является изучение причинно-следственной связи различных факторов и определенного исхода. Однако важно правильно интерпретировать результаты такого анализа: он позволяет оценить риск конкретного события среди пациентов, которые дожили до определенного момента и не подверглись ни одному из конкурирующих событий. Поскольку конкурирующие события игнорируются (цензурируются), непосредственно оценить влияние ковариат на их частоту невозможно. Альтернативой может быть набирающая популярность регрессионная модель конкурирующих рисков Файн и Грей. Этот метод моделирует влияние ковариат на функцию кумулятивной инцидентности и может быть применен, когда целью исследователя является не изучение этиологических ассоциаций, а оценка вероятности каждого из событий - т.е. индивидуальный прогноз. Таким образом, анализ выживаемости может быть проведен при помощи разных методов. Каждый из них не является универсальным, а предназначен для определенных задач, имеет свои преимущества, недостатки и ограничения. Использование оптимального в каждом конкретном случае подхода обеспечит наибольшую объективность исследования.</p></abstract><trans-abstract xml:lang="en"><p>Survival analysis is one of the most widely used methods of statistical analysis. With an imaginary simplicity, this analysis has certain pitfalls. There are different approaches, each of which requires compliance with certain assumptions and a peculiar clinical interpretation. The risk of death can be analysed by directly measuring the relative risk or indirectly assessing it using an odds ratio. However, these estimates are cumulative, do not involve censored observations and do not take into account the time of observation and the impact of covariates. The most common methods of analysis in a case of censored observation are the Kaplan-Meier procedure (which is empirically estimate the probability of surviving a certain time - survival function) and the Nelson-Allen (which is estimate the cumulative hazard function). Both of these methods do not require a priori information about the shape of survival function, however, they allow to estimate the impact on survival (or risk) of only one categorical predictor, cannot correct covariates and are based on the assumption of uninformative censoring. The use of these methods in a case of competing risks gives a deliberately biased assessment of survival. The most widely used method of survival analysis in the presence of competing risks is a cause-specific Cox proportional hazards model. The use of this method is advisable when the researcher aims to study the causal relationship of various factors and a certain outcome. However, it is important to interpret the results of such analysis correctly: it allows to assess the risk of a particular event among patients who have lived to a certain time-point and have not undergone any of the competing events. Because competing events are ignored (censored), it is not possible to directly assess the impact of covariates on their frequency. An alternative may be the increasingly popular Fine and gray competing risks regression model. This method simulates the impact of covariates on the cumulative incidence function and can be applied when the aim of the researcher is not to study the etiological associations, but to estimate the probability of each of the events - i.e. an individual forecast. Thus, the survival analysis can be performed using different methods. Each of them is not universal, and was designed for specific purposes, has its advantages, disadvantages and limitations. The use of the optimal approach in each case will provide the most objective analysis.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>анализ выживаемости</kwd><kwd>статистика</kwd><kwd>трансплантация почки</kwd><kwd>выживаемость реципиентов</kwd><kwd>лист ожидания</kwd><kwd>причинно-специфический риск</kwd><kwd>процедура Каплана-Мейера</kwd><kwd>модель пропорциональных рисков Кокса</kwd><kwd>регрессионная модель Файн и Грей</kwd><kwd>конкурирующий риск</kwd><kwd>survival analysis</kwd><kwd>statistics</kwd><kwd>kidney transplant</kwd><kwd>recipient’s survival rate</kwd><kwd>waiting list</kwd><kwd>cause-specific risk</kwd><kwd>Kaplan-Meier procedure</kwd><kwd>Cox proportional hazards model</kwd><kwd>Fine and Gray competing risk regression model</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Erdoğan S., Gülhan O.T. 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