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From |
Tom Robinson <tomrobnz@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Developing a Predictive Risk Equation from stcox survival analysis |

Date |
Thu, 20 Sep 2012 21:43:46 +1200 |

Thanks very much for the advice, it has all been very helpful Tom On 20 September 2012 02:36, Roger B. Newson <r.newson@imperial.ac.uk> wrote: > I would second Steve's diagnosis. To find more about the issues with the use > of Harrell's c with predictive scores after -stcox- (or even after -streg-), > see Newson (2010). > > Best wishes > > Roger > > References > > Newson RB. Comparing the predictive power of survival models using Harrell’s > c or Somers’ D. The Stata Journal 2010; 10(3): 339–358. Purchase from > http://www.stata-journal.com/article.html?article=st0198 > or download pre-publication draft from > http://www.imperial.ac.uk/nhli/r.newson/papers.htm#papers_in_journals > > > Roger B Newson BSc MSc DPhil > Lecturer in Medical Statistics > Respiratory Epidemiology and Public Health Group > National Heart and Lung Institute > Imperial College London > Royal Brompton Campus > Room 33, Emmanuel Kaye Building > 1B Manresa Road > London SW3 6LR > UNITED KINGDOM > Tel: +44 (0)20 7352 8121 ext 3381 > Fax: +44 (0)20 7351 8322 > Email: r.newson@imperial.ac.uk > Web page: http://www.imperial.ac.uk/nhli/r.newson/ > Departmental Web page: > http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/ > > Opinions expressed are those of the author, not of the institution. > > > On 19/09/2012 15:03, Steve Samuels wrote: >> >> Tom Robinson: >> >>> My problem is that the when I run an estat concordance on my model I get >>> a >>> higher Harrel's C than I do when I run roctab on my outcome and the risks >>> I have calculated (using the development dataset still). >> >> >> >> This is not surprising behavior: -roctab- is for binary outcomes; it >> ignores censoring and time. >> >> Steve >> >> >> >> On Sep 18, 2012, at 9:13 PM, Phil Clayton wrote: >> >> After running your Cox model: >> predict double xbeta, xb >> predict double basesurv, basesurv >> >> Now you need to know the baseline survivor function at 5 years. The >> mistake you've made is that this number is actually the same for everyone. >> Try this: >> line basesurv _t, sort >> >> You just need the point on the curve where _t==5. Since the baseline >> survivor function only goes down with time, this point is the minimum >> basesurv when time is less than 5 years: >> sum basesurv if _t<5 >> scalar base5y=r(min) >> >> Finally you can calculate each patient's risk at 5 years by adjusting the >> baseline risk: >> gen risk5y=1 - base5y^exp(xbeta) >> >> You can of course avoid the use of the scalar, but the above code makes it >> a little clearer what you're doing. Here's the abbreviated version: >> sum basesurv if _t<5 >> gen risk5y=1 - r(min)^exp(xbeta) >> >> A word on precision - you're raising a small number (between 0 and 1) to >> the exponent of another number. Therefore minor problems with precision >> rapidly become very big ones. This is why I have suggested using double >> precision for the xbeta and basesurv variables. It's also very useful to >> centre your covariates, so that the baseline survivor function represents an >> "average" patient rather than a patient with extremely or impossibly low >> risk. See "Making baseline reasonable" in [ST] stcox postestimation. >> >> Phil >> >> On 19/09/2012, at 6:23 AM, Tom Robinson wrote: >> >>> Hi, >>> >>> I am using stcox to develop a predictive risk model but am unsure about >>> how >>> to formulate the final equation. I am using Stata 12.1 >>> >>> I have independent variables that were collected by family physicians as >>> part of routine care e.g. blood pressure, lipids, renal function, >>> demographic variables, time since developing diabetes . These come from a >>> single review and I am using this review date as onset. The outcome is >>> new >>> onset of end-stage renal failure which is collected from a range of >>> national datasets (in New Zealand). >>> >>> I have developed a model using stcox which I'm happy with but need to >>> turn >>> this into a risk prediction equation for risk at 5 years after which I >>> can >>> use in a validation dataset. I have centered all the variables around >>> their >>> mean. >>> >>> What I have done so far is: (following Tangri N, Stevens LA, Griffith J, >>> et >>> al. A predictive model for progression of chronic kidney disease to >>> kidney >>> failure. JAMA. 2011;305(15):1553-1559.appendix) >>> >>> - use predict *newvar*, xb to calculate each individuals overall hazard >>> coefficient >>> - confirmed for myself that this is equivalent to the sum of each >>> variable multiplied by its coefficient from the model >>> - confirmed that a dummy individual X with all the independent >>> variables >>> set at 0 (in other words at the means) has a overall hazard of 0 >>> (*newvar >>> *) >>> - used predict *newvar2*, basesurv to calculate the baseline survivals >>> - set individual X _t to 5 years which is the time period I'm >>> interested >>> in predicting risk at. This individuals baseline survival is Y >>> - Used this survival in the equation gen risk5yr=1-(Y)^exp(*newvar*) to >>> calculate each persons risk of the event at 5 years >>> >>> My problem is that the when I run an estat concordance on my model I get >>> a >>> higher Harrel's C than I do when I run roctab on my outcome and the risks >>> I have calculated (using the development dataset still). >>> >>> I have also run a calibration analysis on my calculated risks which is >>> wildly wrong (the predicted risks in each decile are about half of the >>> actual risks) >>> >>> Clearly I'm doing something wrong but I can't see what. Thanks for any >>> advice >>> >>> -- >>> *Tom Robinson* >>> * >>> * 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/ >> >> >> >> * >> * 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/ >> >> >> * >> * 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/ >> > * > * 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/ -- Tom Robinson ph. 445 2056 mob. 021 482 301 * * 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/

**References**:**st: Developing a Predictive Risk Equation from stcox survival analysis***From:*Tom Robinson <tomrobnz@gmail.com>

**Re: st: Developing a Predictive Risk Equation from stcox survival analysis***From:*Phil Clayton <philclayton@internode.on.net>

**Re: st: Developing a Predictive Risk Equation from stcox survival analysis***From:*Steve Samuels <sjsamuels@gmail.com>

**Re: st: Developing a Predictive Risk Equation from stcox survival analysis***From:*"Roger B. Newson" <r.newson@imperial.ac.uk>

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