On Thu, 15 Jan 2004 08:37:09 -0500 (EST) Alan Zaslavsky <[email protected]> wrote:
> > > Subject: IMPUTE: The art of imputation: thinking about MAR > > Date: Wed, 14 Jan 2004 10:09:16 -0600 > > From: "Howells, William" <[email protected]> > > > > We put a lot of thought into > > building the imputation model and were careful to include other > > covariates that were highly correlated with X2 and all those that we > > want in the analysis model (note: did not include time to death > > because of censoring and not MVN). > > This is the feature of the analysis that most concerns me. Essentially > what this does is to assume conditional independence of X2 and time to > death given the other variables, which of course attenuates the > relationship when you analyze a dataset including both observed and > imputed values of X2. (The impact of this on estimated effect of the > variable of interest X1 is not at all obvious, although you might be > able to figure it out from looking at relationships of X1 and X2, etc.) > I appreciate that modeling missing data with censored survival data is > nonstandard and therefore messy (perhaps impossible to do "correctly" > with any available standard software), but you are better off including > this crucial relationship with some kind of approximate model than > leaving it out altogether. > > To do this using PROC MI, one idea would be to create a few indicators > for survival for 3 months, 6 months, 9 months, etc. (or whatever is > appropriate to the time scale of your disease process). Censored > observations have missing indicators for the time points later than time > of censoring. Then throw this into PROC MI. You will not use the > imputed values of the missing indicators, but this is a mechanism for > using the censored survival data within an MVN imputation framework. > (There might be some obvious reason why this won't work, but try it and > see.) Of course this model is "wrong" but if higher X2 is actually > associated with better prognosis, then using the outcomes in predicting > X2 should help to predict this. Alan - this makes a lot of sense. I wonder whether a simpler approach would also work: include the censoring/event time, event indicator, and product of these two in an imputation model. In imputation methods that allow for nonlinear effects, nonlinear terms could also be added. -Frank --- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University
