I would be very interested to hear ideas about computing
combined summary statistics (R-square, ROC area, etc.) and
whether there is any hope of getting a combined
imputation-adjusted likelihood ratio test. Also,
has anyone seen a reference where model diagnostics
are developed in the multiple imputation framework?
For example, would it be a good idea to make 5
partial residual plots for 5 completed datasets,
and is there a reasonable way to combine these?
Frank Harrell
"Raab, Gillian" wrote:
>
> Its all in Shafer's book - or various other places. Just combine the within
> and between imputation variance
> with simple formulae. What are you using to do computations? If it is SAS I
> have a pretty basic macro I have written that
> produces tables of oods-ratios and 95% confidence intervals from a SAS data
> set that contains all the imputed
> data. Most willing top pass on if it would help.
>
> I'd also be interested to hearing from anyone else who has been trying out
> the new SAS imputation procedures.
>
> Gillian Raab, Napier University, Edinburgh, Scotland
>
> -----Original Message-----
> From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]]
> Sent: 08 September 2001 10:19
> To: [EMAIL PROTECTED]
> Subject: IMPUTE: (no subject)
>
> Dear all
>
> I have a logisitic regression model with continious and categorical
> variables. I carried out multiple imputation for missing values in most of
> the
> variables and redone the logisitic regression.
>
> Now I have 5 results my question is how to apply rubin's rules to combine
> the
> results, I found out it is straight forward for the odds ratios, the
> coeffecients and the standard deviations, I am only stuck with the p-values
> and the 95% confidence intervals. I would like to mention that my data
> consist of 23000 records so assumption of normality is quite feasiable.
>
> Ula Nur
--
Frank E Harrell Jr Prof. of Biostatistics & Statistics
Div. of Biostatistics & Epidem. Dept. of Health Evaluation Sciences
U. Virginia School of Medicine http://hesweb1.med.virginia.edu/biostat
