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From |
roland andersson <rolandersson@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Analysing compound data types from imputed variables |

Date |
Wed, 3 Aug 2011 17:29:53 +0200 |

We are analysing the discriminating capacity of a clinical score, which is constructed from 7 variables. One variable is the ratio of two variables, two are binary, one is an ordered variabel with 4 levels, and finally we have 4 continous variables which have been divided into 2-3 intervals. We now want to 1) validate the score on new data and 2) analyse if some additional variables can add discriminating capacity to the original score. Our problem is missing variables. It seems the missingnes is random. We want to impute these missing values. This is straightforward using ice or mi and using the passive alternative for the ratio-variable. The problem is how to calculate and analyse the score for the cases with missing variables. As we need to divide the continous variables into intervals after imputation we can not use passive. I am thinking to construct the imputed dataset, calculate the score for each imputed dataset according to the intervals of the continous variables. I suppose I can use mim for the analysis of the discriminating capacity of the score. Is there any other alternatives? Should we use the variables after dividing into intervals and then do the imputation instead? It would then be possible to use passive imputation for the score. Other proposals or comments are wellcome Roland Andersson * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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