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From |
"Daniel Waxman" <dan@amplecat.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: Does my statistic for "net proportion of subjects with improved prediction" already exist? |

Date |
Thu, 27 Sep 2007 17:53:22 -0400 |

representing 1 perfect reassignment of risk (new model assigns a higher predicted probability to all those who have events and a lower probability to all those who do not), to -1 for perfect mis-reassignment (the converse). -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Daniel Waxman Sent: Thursday, September 27, 2007 12:31 PM To: statalist@hsphsun2.harvard.edu Subject: st: Does my statistic for "net proportion of subjects with improved prediction" already exist? Statalist, I am studying the effect of adding a biomarker to an existing model and want to describe the effect of that model vis-à-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 No virus found in this outgoing message. Checked by AVG Free Edition. Version: 7.5.488 / Virus Database: 269.13.32/1033 - Release Date: 9/27/2007 11:06 AM * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ No virus found in this incoming message. Checked by AVG Free Edition. Version: 7.5.488 / Virus Database: 269.13.32/1033 - Release Date: 9/27/2007 11:06 AM No virus found in this outgoing message. Checked by AVG Free Edition. Version: 7.5.488 / Virus Database: 269.13.32/1033 - Release Date: 9/27/2007 11:06 AM * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Does my statistic for "net proportion of subjects with improved prediction" already exist?***From:*"Daniel Waxman" <dan@amplecat.com>

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