title: [Statistical inference in the presence of missing data].
abstract: In experimental studies and particularly in medical research the missing data frequently ap-pear. Their existence can strongly affect the conclusions drawn. Therefore, it is important to treat them in appropriate way. This paper discusses three ways of dealing with missing values using example of simulated data and compares the results obtained in each case. It was found that a properly conducted multiple imputation technique gives results most consistent with those that would be obtained if the lack of data does not exist.
authors: Katedra i Zakład Informatyki i Statystyki Uniwersytetu Medycznego w Poznaniu. jmoczko@amp.edu.pl
authors: Jerzy A
authors: JA
authors: Moczko
mesh_terms: D015992:Body Mass Index; D003627:Data Interpretation, Statistical; D016015:Logistic Models; D012107:Research Design