Multiple Imputation in mice/norm

Emmanuel Charpentier wrote:

Le vendredi 24 avril 2009 ? 14:11 -0700, ToddPW a ?crit :

I'm trying to use either mice or norm to perform multiple  
imputation to fill
in some missing values in my data.  The data has some missing  
values because
of a chemical detection limit (so they are left censored).  I'd  
like to use
MI because I have several variables that are highly correlated.   
In SAS's
proc MI, there is an option with which you can limit the imputed  
values that
are returned to some range of specified values.  Is there a way to  
limit the
values in mice?

You may do that by writing your own imputation function and assign  
them
for the imputation of particular variable (see argument
"imputationMethod" and details in the man page for "mice").

                If not, is there another MI tool in R that will  
allow me to
specify a range of acceptable values for my imputed data?

In the function amelia (package "Amelia"), you might specify a  
"bounds"
argument, which allows for such a limitation. However, be aware that
this might destroy the basic assumption of Amelia, which is that your
data are multivariate normal. Maybe a change of variable is in  
order (e.
g. log(concentration) has usually much better statistical properties
than concentration).
Frank Harrell's aregImpute (package Hmisc) has the "curtail" argument
(TRUE by default) which limits imputations to the range of observed
values.
But if your left-censored variables are your dependent variables (not
covariates), may I suggest to analyze these data as censored data, as
allowed by Terry Therneau's "coxph" function (package "survival") ?  
code
your "missing" data as such a variable (use :
coxph(Surv(min(x,<yourlimit>,na.rm=TRUE),
          !is.na(x),type="left")~<Yourmodel>) to do this on-the-fly).
Another possible idea is to split your (supposedly x) variable in  
two :
observed (logical), and value (observed value if observed, <detection
limit> if not) and include these two data in your model. You probably
will run into numerical difficulties due to the (built-in total
separation...).
HTH,
					Emmanuel Charpentier

Thanks for the help,
Todd

All see

@Article{zha09non,
 author =               {Zhang, Donghui and Fan, Chunpeng and Zhang,  
Juan and Zhang, {Cun-Hui}},
 title =                {Nonparametric methods for measurements  
below detection limit},
 journal =      Stat in Med,
 year =                 2009,
 volume =       28,
 pages =        {700-715},
 annote =       {lower limit of detection;left censoring;Tobit  
model;Gehan test;Peto-Peto test;log-rank test;Wilcoxon test;location  
shift model;superiority of nonparametric methods}
}


-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                    Department of Biostatistics   Vanderbilt  
University