I am conducting a discrete-time hazard analysis using Stata 14.0. I am working with Prof. Jenkins material*.
To me, my baseline hazard appears to lead to a non-parametric approach. Those are my coefficients (odds ratios) for the baseline hazard (one unit is one year) and some are empty.
Here is the problem: I want to attain a graph of my hazard function. However, because I have about a douzen covariates and about 45 spells for my baseline hazard variable, I think I need to do out-of-sample-prediction. According to Prof. Jenkins, this means that I need to have a underlying parametric functional form of my baseline hazard** (because the -predict- command will extrapoliate).
But looking at my baseline hazard coefficients, I do not see how I can establish a parametric functional form without using some higher-order polynomial function.
My question therefore is, what would be a good approach in this case? Could I also use a piecewise-constant functional form as a semi-parametric approach and still use out-of-sample-prediction?
I am very grateful for your input. Thank you in advance!
*https://www.iser.essex.ac.uk/resourc...sis-with-stata
**https://www.iser.essex.ac.uk/files/t...s/ec968st6.pdf (page 14ff)
To me, my baseline hazard appears to lead to a non-parametric approach. Those are my coefficients (odds ratios) for the baseline hazard (one unit is one year) and some are empty.
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event | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- c | 0 | 1 (empty) 1 | 1.553132 2.016981 0.34 0.735 .1218407 19.79815 2 | 1.378796 1.792739 0.25 0.805 .1078326 17.6299 3 | .862181 1.121018 -0.11 0.909 .0674305 11.02403 4 | .8890559 1.156885 -0.09 0.928 .0693909 11.39084 5 | .7423318 .9660042 -0.23 0.819 .0579323 9.512084 6 | .7198207 .9404603 -0.25 0.801 .0556048 9.318292 7 | .9072992 1.190255 -0.07 0.941 .0693566 11.86897 8 | .7818488 1.024988 -0.19 0.851 .0598708 10.21011 9 | .5215442 .6845979 -0.50 0.620 .0398082 6.832967 10 | .644339 .8533693 -0.33 0.740 .048059 8.638823 11 | 1.204835 1.592372 0.14 0.888 .0903515 16.06644 12 | .8921704 1.195162 -0.09 0.932 .0645904 12.32332 13 | .5165349 .6978924 -0.49 0.625 .0365627 7.297289 14 | .4082628 .5556115 -0.66 0.510 .0283482 5.879682 15 | .4803247 .6430928 -0.55 0.584 .0348246 6.624971 16 | 1.056018 1.412588 0.04 0.967 .0767462 14.53067 17 | .8581945 1.180493 -0.11 0.911 .0579044 12.7192 18 | 1.024795 1.409555 0.02 0.986 .0691593 15.18529 19 | 1.203183 1.741118 0.13 0.898 .0705608 20.51633 20 | .3300895 .6263513 -0.58 0.559 .0080068 13.60828 21 | .1806894 .2850472 -1.08 0.278 .0082057 3.978769 22 | 1.126723 1.597316 0.08 0.933 .0700002 18.13574 23 | .2355325 .3772079 -0.90 0.367 .0102057 5.435749 24 | 1 (empty) 25 | 6.051808 10.6546 1.02 0.306 .1919951 190.7569 26 | 1 (empty) 27 | 1.522701 2.443217 0.26 0.793 .0655899 35.35021 28 | 1 (empty) 29 | 1 (empty) 30 | 1 (empty) 33 | 1 (empty) 35 | 1 (empty) 36 | 1 (empty) 41 | 1 (omitted) 42 | 1 (empty) 44 | 1 (empty)
Here is the problem: I want to attain a graph of my hazard function. However, because I have about a douzen covariates and about 45 spells for my baseline hazard variable, I think I need to do out-of-sample-prediction. According to Prof. Jenkins, this means that I need to have a underlying parametric functional form of my baseline hazard** (because the -predict- command will extrapoliate).
But looking at my baseline hazard coefficients, I do not see how I can establish a parametric functional form without using some higher-order polynomial function.
My question therefore is, what would be a good approach in this case? Could I also use a piecewise-constant functional form as a semi-parametric approach and still use out-of-sample-prediction?
I am very grateful for your input. Thank you in advance!
*https://www.iser.essex.ac.uk/resourc...sis-with-stata
**https://www.iser.essex.ac.uk/files/t...s/ec968st6.pdf (page 14ff)
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