Re: Missing covariates

From: "diane r mould" <drmould@attglobal.net> Subject: Re: Missing covariates Date: Mon, 2 Jul 2001 11:51:15 -0400 Dear Atul Substitution of the median values for missing covariates is not a good idea, particularly when you have a large percentage of missing data. If the missing data is replaced with the median data, the median of the covariate distribution is preserved, but the other aspects of it are not (ie variance quantiles etc). This can lead to problems (e.g. accepting a false covariate or rejecting a true one) when covariate models are tested. The use of joint functions is a lot better than replacing missing data with the median data. If the use of a joint function is not feasible (they can be difficult to get to run and often take a long time to converge), try doing multiple imputation. If you are not sure how to impliment these methods, please let me know and I can help out a bit, as I have been struggling a lot lately with these missing data problems Best Regards diane