> Dear all ,
Thanking you in advance
> I am reanalysing a data set with 750 subjects and 15 variables, 2
> categorical ( variables include age smoking alcohol and exercise
> cholesterol, triglycerides, and other metabolic markers) about 25 % of
> subjects are missing from upto 6 of these variables at baseline. Sometimes
> data is missing by design, for example 10% of subjects did not have
> insulin measured because of a incompatable test. Additionally I have data
> on 500 subjects at a second follow-up visit 2 years later. Subjects are
> then followed up to the onset of coronary heart disease.
> The data is observational, in that there was no intended intervention
> other than being screened every 2 years. There may well have been doctor's
> advice to lose weight, drink less or see a specialist, but the advice
> given is not very well recorded.
>
> The original analysis was to estimate missing values from the remainder of
> the baseline variables using STATA's "impute" command. ("impute" predicts
> the missing values using multiple regression for data with different
> patterns of missing data)
> Then on the imputed values perform a factor analysis on some of the
> variables to extract 2 factors, and then do cox regression on these 2
> factors and the remainder of the variables.
>
> However I have since thought that one could use the data from the second
> visit for the estimation of missing values. Either by direct substitution,
> and adjusting the survival/ censorship time. Or by estimating the
> baseline missing values using both the follow-up values and the
> non-missing follow-up values in "impute". However after two years
> lifestyle changes may have affected the levels of the variables.
>
> * If one has follow-up data is it allowable to estimate baseline data
> from the follow-up data in addition to other baseline data?
> * Should one estimate the missing Insulin?
> * I also wonder if it would be better to estimate the factors rather
> than the missing components. This is suggested in the STATA manual.
>
Please respond to [log in to unmask] I will post a summary of responses to
allstat
> Jim Jeffs
>
>
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