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
