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Re: st: Calculation of cubic splines
From
Nick Cox <[email protected]>
To
[email protected]
Subject
Re: st: Calculation of cubic splines
Date
Thu, 19 May 2011 14:07:37 +0100
You need to look at the source code of -mkspline- and the Harrell
reference given in the manual.
Nick
On Thu, May 19, 2011 at 1:57 PM, Mikkel Brabrand <[email protected]> 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.
>
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