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Multiple imputations for missing data in lifecourse studies

Listed author(s):
  • Bianca L. De Stavola


    (London School of Hygiene and Tropical Medicine)

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    Missing imputation (MI) is a method to deal with missing at random (MAR) data. It is a Monte Carlo procedure where missing values are replaced by several (usually less than 10) simulated versions. It consists of three steps (Shafer, 1999): i. generation of the imputed values for the missing data; ii. analysis of each imputed data set where missing observations are replaced by imputed ones; iii. combination of the results from all imputed data sets. The procedure is easily implemented in Stata for univariate normally distributed missing variables. Extensions to the case of multivariate normal variables - often encountered in life course epidemiology - will be discussed.

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    Paper provided by Stata Users Group in its series United Kingdom Stata Users' Group Meetings 2003 with number 08.

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    Date of creation: 16 Mar 2003
    Handle: RePEc:boc:usug03:08
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