Missing data and multiple imputation in clinical epidemiological research
نویسندگان
چکیده
منابع مشابه
Missing data and multiple imputation in clinical epidemiological research
Missing data are ubiquitous in clinical epidemiological research. Individuals with missing data may differ from those with no missing data in terms of the outcome of interest and prognosis in general. Missing data are often categorized into the following three types: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). In clinical epidemiological resea...
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1Department of Social Medicine, University of Bristol, Bristol BS8 2PR 2MRC Biostatistics Unit, Institute of Public Health, Cambridge CB2 0SR 3Clinical Epidemiology and Biostatistics Unit, Murdoch Children’s Research Institute, and University of Melbourne, Parkville, Victoria 3052, Australia 4Cancer and Statistical Methodology Groups, MRC Clinical Trials Unit, London NW1 2DA 5Medical Statistics...
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Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...
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In many fields, including the field of nephrology, missing data are unfortunately an unavoidable problem in clinical/epidemiological research. The most common methods for dealing with missing data are complete case analysis-excluding patients with missing data--mean substitution--replacing missing values of a variable with the average of known values for that variable-and last observation carri...
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ژورنال
عنوان ژورنال: Clinical Epidemiology
سال: 2017
ISSN: 1179-1349
DOI: 10.2147/clep.s129785