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      How can I deal with missing data in my study?

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      Australian and New Zealand journal of public health

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          Abstract

          Missing data in medical research is a common problem that has long been recognised by statisticians and medical researchers alike. In general, if the effect of missing data is not taken into account the results of the statistical analyses will be biased and the amount of variability in the data will not be correctly estimated. There are three main types of missing data pattern: Missing Completely At Random (MCAR), Missing At Random (MAR) and Not Missing At Random (NMAR). The type of missing data that a researcher has in their dataset determines the appropriate method to use in handling the missing data before a formal statistical analysis begins. The aim of this practice note is to describe these patterns of missing data and how they can occur, as well describing the methods of handling them. Simple and more complex methods are described, including the advantages and disadvantages of each method as well as their availability in routine software. It is good practice to perform a sensitivity analysis employing different missing data techniques in order to assess the robustness of the conclusions drawn from each approach.

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          Author and article information

          Journal
          Aust N Z J Public Health
          Australian and New Zealand journal of public health
          1326-0200
          1326-0200
          Oct 2001
          : 25
          : 5
          Affiliations
          [1 ] Department of Medicine, University of Auckland, New Zealand. d.bennett@ctru.auckland.ac.nz
          Article
          10.1111/j.1467-842X.2001.tb00294.x
          11688629
          144795f6-1a60-4b20-836b-31665c5c5c48
          History

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