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S Feb 13, 2020 at 14:59 history suggested dpel CC BY-SA 4.0
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Feb 13, 2020 at 12:10 review Suggested edits
S Feb 13, 2020 at 14:59
Apr 12, 2013 at 22:20 answer added Stef van Buuren timeline score: 11
Apr 12, 2013 at 12:24 history edited Robert Long
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Jul 12, 2012 at 22:58 comment added atiretoo Sorry, I just realized there's a typo in my comment, should be tumor size == 0, so your response answers the question, thanks. In your additional code the reason why rows with overall_tumor_grade == NA are set to NA, is that any comparison with NA results in NA, not TRUE or FALSE. You can get around that by picking out the rows with tumor_size == 0, hence my botched question about the data above.
Jul 12, 2012 at 20:42 history edited JJM CC BY-SA 3.0
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Jul 12, 2012 at 20:08 comment added JJM @longrob all of the patients that have tumor sizes of 0 are already coded as grade 'X'. All of the missing overall_tumor_grade observations have tumor sizes >0, and the problem I'm having is that MICE is imputing some of those NAs as grade 'X'.
Jul 12, 2012 at 19:38 comment added Robert Long @atiretoo I don't see why that assumption is needed ? If there are any observations with tumor_size==0 and overall_tumor_grade==NA just recode those NAs as X. Then delete observations with overall_tumor_grade==NA and run the imputations..... ?
Jul 12, 2012 at 19:25 comment added JJM We cannot assume that because by definition grade 'X' means that the tumor size=0
Jul 12, 2012 at 18:01 answer added Robert Long timeline score: 7
Jul 12, 2012 at 18:00 comment added atiretoo Can we assume that all instances of overall_tumor_grade == 'X' also have tumor_size == 1? I think the answer might be to construct a data.frame without any rows with 'X', and then do the imputation on that, but that only works if the above assumption is true.
Jul 12, 2012 at 16:43 history asked JJM CC BY-SA 3.0