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From |
Richard Goldstein <richgold@ix.netcom.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Comparing risk scores |

Date |
Tue, 18 Oct 2011 08:22:20 -0400 |

Karin, I suggest you might want to read Sullivan, LM, et al. (2004), "Presentation of multivariate data for clinical use: the Framingham study risk score functions," _Statistics in Medicine_, 23: 1631-1660, which describes how the Framingham people came up with their risk scores Rich On 10/18/11 8:18 AM, K Jensen wrote: > Hi Nick > > Thanks for your reply. It's actually a bit more complicated than > that. We are trying to construct a "best" single score that would be > simple and used clinically. The elements that are summed to make the > score (0,1,2,3 etc) are derived from various clinical measurements. > They are dichotomised by choosing the cutpoint that maximises the sum > of sensitivity+specificity. Only those binary variables significant > in a univariate logistic regression are proposed for the model. > > I am wanting to choose the "best" model, that is useful for > clinicians. If we had 7 binary variables, say, I would look at all > possibilities of choosing different combinations of the sums of them. > E.g. 1, 2, 3, 4, 5, 6, 7,1+2,1+3,1+4,1+5,1+6,1+7, 2+3, 2+4,... up to > 1+2+3+4+5+6+7. I would like to use the optimal score based on this > method, but don't know how to measure optimality. > > Best wishes, > > Karin > > On 18 October 2011 12:36, Nick Cox <njcoxstata@gmail.com> wrote: >> I would recast this as a -logit- or -logistic- problem in which your >> outcome is dead or alive. Depending on how you think about your >> scores, they define predictors to be treated as they come or >> predictors to be treated as a set of indicator variables (or in some >> cases both). >> >> I don't think you are restricted to using one score or the other as predictor. >> >> Nick >> >> On Tue, Oct 18, 2011 at 12:11 PM, K Jensen <k.x.jensen@gmail.com> wrote: >>> Maybe this is more of a stats question than a Stata one, but there are >>> such a lot of good brains here... >>> >>> We are constructing point scores to indicate severity of risk Death >>> is the outcome. What is the best way of measuring the usefulness of >>> the score? The aim is to show a good gradient of risk. Say the >>> results for two different scores were: >>> >>> Score Dead Alive %dead Totals >>> 0 12 136 9.9% 145 >>> 1 18 126 15.4% 144 >>> 2 18 62 26.2% 81 >>> 3 10 9 57.1% 20 >>> 4 2 0 100 % 3 >>> ------------------------------------- >>> Total: 60 333 393 >>> >>> Score Dead Alive %dead Totals >>> 0 8 174 4.6% 182 >>> 1 21 143 12.8% 164 >>> 2 22 19 53.7% 41 >>> 3 5 1 83.3% 6 >>> ------------------------------------- >>> TOTAL: 60 333 393 >>> >>> Which is the better score? What is the best way to measure its >>> predictive power? I understand that ROC type analysis doesn't really >>> apply here. Some measure of R-squared? AIC? >>> >>> Thankyou >>> >>> Karin >>> >>> PS) I have made up the data, so the numbers don't quite add up. It is >>> meant to be two different, competing scores on the same people. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Comparing risk scores***From:*K Jensen <k.x.jensen@gmail.com>

**References**:**st: Comparing risk scores***From:*K Jensen <k.x.jensen@gmail.com>

**Re: st: Comparing risk scores***From:*Nick Cox <njcoxstata@gmail.com>

**Re: st: Comparing risk scores***From:*K Jensen <k.x.jensen@gmail.com>

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