Submitted on behalf of Bill Howells: >>> "Howells, William" <[EMAIL PROTECTED]> 4/8/2004 4:16:54 PM >>> We have a regression model that uses a risk score that is calculated as a weighted sum of 10 prognostic variables. The weights were obtained from a regression analysis on another sample. In my sample of n=800, one or more of the 10 individual variables is missing in 40% of the patients, resulting in 40% missing risk score. I calculated the risk score on the observed data and then imputed the missing risk score using the 10 variables, in addition to other var in the analysis. Later, I found out that statisticians at our data center at another institution had imputed the 10 variables and then calculated the risk score on the imputed data. The combined inferences from the two approaches are not wildly different, eg. hazard ratio of 2.5 (p=0.005) vs 2.7 (p=0.003), but I wonder if there is any theory or simulations to help decide which approach is better?
Bill Howells, MS Behavioral Medicine Center Washington University School of Medicine St Louis, MO
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