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<article 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" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Regional Anesthesia and Acute Pain Management</journal-id><journal-title-group><journal-title xml:lang="en">Regional Anesthesia and Acute Pain Management</journal-title><trans-title-group xml:lang="ru"><trans-title>Регионарная анестезия и лечение острой боли</trans-title></trans-title-group></journal-title-group><issn publication-format="print">1993-6508</issn><issn publication-format="electronic">2687-1394</issn><publisher><publisher-name xml:lang="en">Eco-Vector</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">42843</article-id><article-id pub-id-type="doi">10.17816/RA42843</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Articles</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Статьи</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Data missing: how to solve and how to escape the problem</article-title><trans-title-group xml:lang="ru"><trans-title>Пропуск данных в выборке: как решать проблему и как ее избежать</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><name-alternatives><name xml:lang="en"><surname>Tikhova</surname><given-names>Galina P.</given-names></name><name xml:lang="ru"><surname>Тихова</surname><given-names>Галина Петровна</given-names></name></name-alternatives><bio xml:lang="en"><p>senior researcher, Laboratory of clinical epidemiology, Institute of highest biomedical technologies, Petrozavodsk State University</p></bio><bio xml:lang="ru"><p>научный сотрудник лаборатории клинической эпидемиологии Института высоких биомедицинских технологий, ГБОУ ВПО «Петрозаводский государственный университет»</p></bio><email>tikhovag@gmail.com</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Petrozavodsk State University named after O.V. Kuusinen</institution></aff><aff><institution xml:lang="ru">ГБОУ ВПО «Петрозаводский государственный университет»</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2016-09-15" publication-format="electronic"><day>15</day><month>09</month><year>2016</year></pub-date><volume>10</volume><issue>3</issue><issue-title xml:lang="en">VOL 10, NO3 (2016)</issue-title><issue-title xml:lang="ru">ТОМ 10, №3 (2016)</issue-title><fpage>205</fpage><lpage>209</lpage><history><date date-type="received" iso-8601-date="2020-08-24"><day>24</day><month>08</month><year>2020</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2016, Eco-Vector</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2016, ООО "Эко-Вектор"</copyright-statement><copyright-year>2016</copyright-year><copyright-holder xml:lang="en">Eco-Vector</copyright-holder><copyright-holder xml:lang="ru">ООО "Эко-Вектор"</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/" start_date="2020-10-01"/></permissions><self-uri xlink:href="https://rjraap.com/1993-6508/article/view/42843">https://rjraap.com/1993-6508/article/view/42843</self-uri><abstract xml:lang="en"><p>The article is devoted to the problem of missing data in clinical trials and clinical studies. The author considered three mechanisms of generating of missing data in collected sample. Each mechanism type is reviewed in details in terms of its effects on sample representativeness and the magnitude of result bias. The ways to reduce probability and amount of missing data are pointed in the phase of planning and on the stage of statistical data processing and inference.</p></abstract><trans-abstract xml:lang="ru"><p>Статья посвящена проблеме пропуска данных в клинических исследованиях и испытаниях. Рассмотрены три механизма, ответственных за возникновение пропущенных данных в выборке. Подробно рассмотрен каждый из них, его влияние на репрезентативность выборки и величину смещения результатов. Указаны пути снижения вероятности и количества пропущенных данных на этапе планирования исследования и на стадии статистической обработки и формулирования заключений.</p></trans-abstract><kwd-group xml:lang="en"><kwd>clinical study</kwd><kwd>missing data</kwd><kwd>MCAR</kwd><kwd>MNAR</kwd><kwd>MAR</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>клиническое исследование</kwd><kwd>пропуск данных</kwd></kwd-group></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Mirkes E.M., Coats, T.J., Levesley J., Gorban, A.N. Handling missing data in large healthcare dataset: A case study of unknown trauma outcomes. Computers in Biology and Medicine. 2016; 75: 203-16.</mixed-citation></ref><ref id="B2"><label>2.</label><mixed-citation>Тихова Г.П. Планируем клиническое исследование. Вопрос 2: Выбор конечных точек. Регионарная анестезия и лечение острой боли. 2014; 10(4): 67-70.</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>Enders C.K. Applied Missing Data Analysis. New York: Guilford Press; 2010</mixed-citation></ref><ref id="B4"><label>4.</label><mixed-citation>Rubin D.B. Inference and Missing Data. Biometrika.1976; 63(3): 581-92.</mixed-citation></ref></ref-list></back></article>
