Statalist,
I am studying the effect of adding a biomarker to an existing model and want
to describe the effect of that model vis-�-vis the number of subjects with
improved predictions in the �new model� vs. the �old model�. While there is
an extensive literature on this topic, most of it divides the outcome into
risk categories (i.e. predicted risk of 0-5%, 5-10%, etc.), something that I
am not so interested in doing.
An intuitive way to look at this would be to look at the net number of
subjects who are assigned a higher predicted probability with the new model
among those with the outcome in question, plus the net number assigned a
lower probability among those who did not have the outcome. The ratio of
this number to the total # of subjects would then be the proportion of
patients with improved predictions (and would range from zero to 1). See
example below.
My question: Did I just reinvent the wheel? (e.g. is this equivalent to
some existing statistic?) Does anybody see any logical problem with looking
at this as one measure of the effect of adding a predictor to an existing
model?
Thanks,
Daniel Waxman
**** example: (where zlog is continuous, zero is dichotomous, new_marker is
the dichotomous new marker, and there is no missing data) ***
. logistic outcome zlog zero
. predict p_old
. logistic outcome zlog zero new_marker
. predict p_new
. count if e(sample)
. gen N=r(N)
. egen number_up_outcome=total(p_new>p_old & outcome)
. egen number_down_outcome=total(p_new<p_old & outcome)
. egen number_up_no_outcome=total(p_new>p_old & !outcome)
. egen number_down_no_outcome=total(p_new<p_old & !outcome)
. gen net_proportion_improved=
((number_up_outcome-number_down_outcome)+(number_down_no_outcome-number_up_n
o_outcome))/N
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