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From |
Roger Newson <r.newson@imperial.ac.uk> |

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

Subject |
Re: st: Calculation of cubic splines |

Date |
Thu, 19 May 2011 14:45:38 +0100 |

I hope this helps. Best wishes Roger 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 19/05/2011 14:08, Maarten Buis wrote:

You already have the predicted probabilities, that is what you got when you typed -predict p_spline-. The analytic representation is not so easy, so that I cannot meaningfully start trying to type it here in plain text. I would just look at the methods and formulas section of the manual entry on -mkspline-(*). Alternatively I often like linear splines as a good compromise between interpretability and flexibility of the curve. Hope this helps, Maarten (*) Note however that when you are using Stata 10, the manual entry contains a typo in the formula. I believe a minus sign on the wrong side of one of the brackets. On Thu, May 19, 2011 at 2:57 PM, Mikkel Brabrand<mikkel@brabrand.net> wrote:All. I am trying to use cubic splines to assess risk of in-hospital mortality using some vital signs. I would like to calculate the predicted mortality using cubic splines manually. I have defined the following knots: . mkspline _Ssbt = sbt, cubic nknots(5) displayknots | knot1 knot2 knot3 knot4 knot5 -------------+------------------------------------------------------- sbt | 97 119 132 146 178 . mat sbt_knots = r(knots) . mkspline _Stemp = temp, cubic nknots(5) displayknots | knot1 knot2 knot3 knot4 knot5 -------------+------------------------------------------------------- temp | 35.9 36.6 37 37.3 38.8 . mat temp_knots = r(knots) . mkspline _Salder = alder, cubic nknots(5) displayknots | knot1 knot2 knot3 knot4 knot5 -------------+------------------------------------------------------- alder | 23 53 66 76 88 . mat alder_knots = r(knots) And have run the logistic regression as follows: . xi: logit in_hosp_mort _Ssbt* _Stemp* _Salder*, or Iteration 0: log likelihood = -364.70483 Iteration 1: log likelihood = -322.05257 Iteration 2: log likelihood = -301.68143 Iteration 3: log likelihood = -299.60534 Iteration 4: log likelihood = -299.5029 Iteration 5: log likelihood = -299.50192 Iteration 6: log likelihood = -299.50192 Logistic regression Number of obs = 2979 LR chi2(12) = 130.41 Prob> chi2 = 0.0000 Log likelihood = -299.50192 Pseudo R2 = 0.1788 ------------------------------------------------------------------------------ in_hosp_mort | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Ssbt1 | .9557737 .0141873 -3.05 0.002 .9283678 .9839888 _Ssbt2 | 1.19366 .1618851 1.31 0.192 .9150398 1.557117 _Ssbt3 | .4041954 .3164691 -1.16 0.247 .0871232 1.875205 _Ssbt4 | 3.502106 4.366206 1.01 0.315 .3041618 40.32311 _Stemp1 | .2456296 .0807298 -4.27 0.000 .1289796 .4677788 _Stemp2 | 6.086799 31.49901 0.35 0.727 .0002396 154643.8 _Stemp3 | 1.01e+11 3.39e+12 0.75 0.452 2.19e-18 4.62e+39 _Stemp4 | 3.34e-36 2.06e-34 -1.33 0.185 1.19e-88 9.42e+16 _Salder1 | 1.078022 .0906853 0.89 0.372 .9141613 1.271254 _Salder2 | 1.004445 .1631946 0.03 0.978 .7305158 1.381093 _Salder3 | .7612758 .8178836 -0.25 0.800 .0926928 6.252275 _Salder4 | 2.45316 5.18604 0.42 0.671 .0389283 154.5918 ------------------------------------------------------------------------------ . predict p_spline if e(sample) (option pr assumed; Pr(in_hosp_mort)) . . roctab in_hosp_mort p_spline, summary ROC -Asymptotic Normal-- Obs Area Std. Err. [95% Conf. Interval] -------------------------------------------------------- 2979 0.8207 0.0240 0.77361 0.86786 . My question is: What is the formula I should use to calculate the predicted mortality? I have spend a great deal of time on this and have not been able to figure it out. Thanks. Mikkel * * 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/

**References**:**st: Goodness of fit***From:*mikkelbrabrand <mikkel@brabrand.net>

**st: RE: Goodness of fit***From:*"Visintainer, Paul" <Paul.Visintainer@baystatehealth.org>

**st: Calculation of cubic splines***From:*Mikkel Brabrand <mikkel@brabrand.net>

**Re: st: Calculation of cubic splines***From:*Maarten Buis <maartenlbuis@gmail.com>

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