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Nauchno-prakticheskii zhurnal «Patogenez»
Article . 2019 . Peer-reviewed
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Analysis of missing data in experimental medicine: an example of postresuscitation disorders in mononucleotide metabolism

Анализ данных с пропусками в экспериментальной медицине: на примере постреанимационных нарушений обмена мононуклеотидов

Analysis of missing data in experimental medicine: an example of postresuscitation disorders in mononucleotide metabolism

Abstract

Актуальность: В экспериментальной медицине из-за гибели части подопытных животных от изучаемой патологии возникают пропуски в данных. Эти пропуски являются неслучайными и приводят к ошибкам в результатах и выводах исследований, поскольку обычные статистические методы для их обработки не подходят. Необходима разработка новых методов анализа неслучайных пропусков и проверка их применимости в медицинских исследованиях. Цель: испытание ранее разработанного авторами метода анализа данных с неслучайными пропусками на примере исследования постреанимационных нарушений обмена мононуклеотидов. Методы: моделирование на крысах 6,5-минутной асфиксии и постреанимационной болезни, определение в головном мозге крыс содержаний мононуклеотидов, нуклеозидов и азотистых оснований, анализ их спектров. Обработка результатов с помощью статистической программы «CensMed», ранее созданной авторами. Результаты: В первые 30 мин после начала реанимации в головном мозге крыс усиливается катаболизм мононуклеотидов, накапливаются нуклеозиды и азотистые основания. В более поздние сроки постреанимационного периода из-за гибели части животных от изучаемой патологии возникает неопределенность в результатах сравнения оживленных крыс с контрольной группой. Уменьшить эту неопределенность и установить верхнюю и нижнюю границы возможного влияния пропусков удалось при помощи предложенного авторами метода. Определены ограничения, присущие нашему методу. Заключение: Результаты исследований, содержащих неслучайные пропуски, следует обрабатывать не обычными, а специальными статистическими методами. Background: In experimental medicine, some values may be missing from the data due to death of a part of experimental animals from the study pathology. These missing values are not accidental and lead to errors in results and conclusions of the studies, since conventional statistical methods are not suitable for them. It is necessary to develop new methods for analyzing missing data and testing their applicability in medical studies. Aim: Testing a previously developed analytical method for samples with non-random, missing values on an example of a study of postresuscitation disorders in mononucleotide metabolism. Methods: Modeling 6.5-min asphyxia and postresuscitation disease in rats; measurement of mononucleotides, nucleosides and nucleobases in the rat brain; and analysis of their arrays. Processing of results using a CensMed statistical software previously created by the authors. Results: In the first 30 minutes after starting the resuscitation, mononucleotide catabolism increased in the rat brain, which resulted in accumulation of nucleosides and nucleobases. In later post-resuscitation periods, due to death of a part of animals from the study pathology, comparison of resuscitated rats with the control group became ambiguous. Using the method previously proposed by the authors it appeared possible to reduce this ambiguity and to establish upper and lower limits for possible influence of the missing values. The authors defined limitations of the proposed method. Conclusion: The results of studies including non-random, missing values should be processed with special rather than regular statistical methods.

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Keywords

non-random missing data, данные с пропусками, brain, головной мозг, resuscitation, nucleobases, mononucleotides, реанимация, missing data, мононуклеотиды, неслучайные пропуски, азотистые основания, нуклеозиды, nucleosides

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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Average
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