. set more 1 . drop _all . . set obs 200 obs was 0, now 200 . gen int karnofsk = 10+10*int(9.5*uniform()) . gen byte icu = uniform()<.2 . gen int survive = (-10-karnofsk)*log(uniform()) + 1 . gen int t2 = -.15*karnofsk*log(uniform()) . replace survive = t2 + 1 if icu (41 changes made) . gen byte dead = survive<365 . replace survive=365 if !dead (0 changes made) . . summ survive karnofsk Variable | Obs Mean Std. Dev. Min Max ---------+--------------------------------------------------- survive | 200 46.15 61.3805 1 345 karnofsk | 200 49.6 27.79755 10 100 . cox survive karnofsk, dead(dead) Iteration 0: Log Likelihood =-865.73173 Iteration 1: Log Likelihood =-852.31805 Iteration 2: Log Likelihood =-852.31781 Cox regression Number of obs = 200 chi2(1) = 26.83 Log Likelihood =-852.31781 Prob > chi2 = 0.0000 Variable | Coefficient Std. Error t Prob > |t| Mean ---------+-------------------------------------------------------------- survive| 46.15 dead| 1 ---------+-------------------------------------------------------------- karnofsk| -.015111 .0029536 -5.116 0.000 49.6 ---------+-------------------------------------------------------------- . . bailey survive karnofsk, dead(dead) autofix(.01) trace lambda(.1) Iteration 0: Log Likelihood = -959.1589 (1) Variable | alpha gamma delta ---------+------------------------------------------------ _cons | -3.60948 -3.8319 -4.29328 --------------------------------------------------------------------------- Optimal rho = 0.66725 Iteration 1: Log Likelihood = -954.9526 (2) Norm Step = 0.950853 Gradient = 4.65236 Variable | alpha gamma delta ---------+------------------------------------------------ _cons | -3.40132 -2.90455 -4.26474 --------------------------------------------------------------------------- Optimal rho = 1.07064 Iteration 2: Log Likelihood = -954.4905 (2) Norm Step = 0.267542 Gradient = 6.45577 Variable | alpha gamma delta ---------+------------------------------------------------ _cons | -3.58877 -3.09221 -4.29975 --------------------------------------------------------------------------- Optimal rho = 1.00221 Iteration 3: Log Likelihood = -954.4889 (2) Norm Step = 0.010784 Gradient = 13.9842 Predicting interior max(4), step type 2 Variable | alpha gamma delta ---------+------------------------------------------------ _cons | -3.59671 -3.09236 -4.29245 --------------------------------------------------------------------------- Optimal rho = 1.00051 Iteration 4: Log Likelihood = -954.4889 (2) Norm Step = 0.000171937 Gradient = 5.29114 Predicting interior max(5), step type 1 Variable | alpha gamma delta ---------+------------------------------------------------ _cons | -3.5968 -3.0925 -4.29249 --------------------------------------------------------------------------- Iteration 0: Log Likelihood = -954.4889 (1) Variable | alpha gamma delta ---------+------------------------------------------------ karnofsk | 0 0 0 _cons | -3.5968 -3.0925 -4.29249 --------------------------------------------------------------------------- Optimal rho = 1.79156 Iteration 1: Log Likelihood = -941.7631 (2) Norm Step = 1.14589 2nd partial Singularity found for parameter _cons.delta Gradient = 9.69163 Variable | alpha gamma delta ---------+------------------------------------------------ karnofsk | -0.00992044 0.0147717 -0.00137995 _cons | -2.87053 -3.97678 -4.35018 --------------------------------------------------------------------------- Optimal rho = 1.39343 Iteration 2: Log Likelihood = -937.4427 (2) Norm Step = 1.3241 Gradient = 2.46427 Variable | alpha gamma delta ---------+------------------------------------------------ karnofsk | -0.0270914 0.0238682 0.00725208 _cons | -2.3971 -5.01746 -5.01773 --------------------------------------------------------------------------- Optimal rho = 1.01067 Iteration 3: Log Likelihood = -936.9583 (2) Norm Step = 0.526846 Gradient = 1.74486 Variable | alpha gamma delta ---------+------------------------------------------------ karnofsk | -0.0275968 0.0337757 0.00909032 _cons | -2.49207 -5.53199 -5.07855 --------------------------------------------------------------------------- Optimal rho = 1.02993 Iteration 4: Log Likelihood = -936.9427 (2) Norm Step = 0.092392 Gradient = 1.83227 Variable | alpha gamma delta ---------+------------------------------------------------ karnofsk | -0.028676 0.0337894 0.00796737 _cons | -2.47796 -5.52191 -4.98782 --------------------------------------------------------------------------- Optimal rho = 1.00107 Iteration 5: Log Likelihood = -936.9426 (2) Norm Step = 0.00326148 Gradient = 4.77052 Predicting interior max(5), step type 2 Variable | alpha gamma delta ---------+------------------------------------------------ karnofsk | -0.0287192 0.0338422 0.00799253 _cons | -2.47772 -5.52477 -4.98937 --------------------------------------------------------------------------- Optimal rho = 0.999861 Iteration 6: Log Likelihood = -936.9426 (2) Norm Step = 2.22818e-05 Gradient = 0.667265 Predicting interior max(5), step type 1 Variable | alpha gamma delta ---------+------------------------------------------------ karnofsk | -0.0287193 0.0338422 0.00799231 _cons | -2.47772 -5.52475 -4.98935 --------------------------------------------------------------------------- (convergence achieved) Bailey-Makeham Survival Model 0.4 Log Likelihood (C) = -954.489 Number of observations = 200 Chi2( 3) = 35.093 Log Likelihood = -936.943 Prob>chi2 = 0.0000 Structural | parameter Var. | Coef. Std. Err. t Sig. Mean ------------------+---------------------------------------------------------- alpha karnofsk | -0.0287193 0.00898029 -3.198 0.0016 49.6 gamma karnofsk | 0.0338422 0.0177919 1.902 0.0586 49.6 delta karnofsk | 0.00799231 0.0107873 0.741 0.4596 49.6 alpha _cons | -2.47772 0.25908 -9.564 0.0000 1 gamma _cons | -5.52475 1.19964 -4.605 0.0000 1 delta _cons | -4.98935 0.893986 -5.581 0.0000 1 ------------------+---------------------------------------------------------- . msurface Structural | parameter Var. | -2 SE -1 SE +1 SE +2 SE ------------------+---------------------------------------------------------- alpha karnofsk | 6.33( 0.6) 2.01( 0.8) 3.47( 1.3) 18.95( 1.8) gamma karnofsk | 10.10( 0.6) 3.62( 0.9) 2.97( 0.7) 8.46( 0.5) delta karnofsk | 54.75( 0.5) 18.44( 0.7) 36.34( 1.4) 215.19( 2.1) alpha _cons | 6.99( 0.8) 1.98( 0.9) 2.55( 1.1) 11.60( 1.3) gamma _cons | 12.09( 0.5) 4.79( 0.7) 7.46( 1.1) 28.97( 1.1) delta _cons | 61.21( 0.5) 21.45( 0.7) 46.74( 1.5) 290.66( 2.3) ------------------+---------------------------------------------------------- . mpredict , ptime(day30[30]) pparm(alpha[alpha] gamma[gamma] delta[delta]) . end of do-file . summ Variable | Obs Mean Std. Dev. Min Max ---------+--------------------------------------------------- karnofsk | 200 49.6 27.79755 10 100 icu | 200 .205 .4047147 0 1 survive | 200 46.15 61.3805 1 345 t2 | 200 7.025 10.17754 0 69 dead | 200 1 0 1 1 day30 | 200 .5780057 .170887 .3866624 .8592471 alpha | 200 -3.902199 .7983258 -5.349651 -2.764915 gamma | 200 -3.846183 .9407297 -5.186333 -2.140537 delta | 200 -4.592929 .2221665 -4.909424 -4.190116 . exit, clear