expand dataset and the cloglog model version

. use "F:\GERON 701 2014\Lab Session datasets\firstsex.dta"

. gen cases_less1= time-1

*We expand cases so that those with event in year 2 get 2 cases, yr 3 get 3 cases, etc.

. expand cases_less1(642 observations created)

. by id: gen period=_n

. by id: egen last=max(period)

. quietly tabulate period, generate(d_)

. gen event=0

. replace event=1 if censor==0 & last==period(126 real changes made)

. logit event d_1 d_2 d_3 d_4 d_5 d_6 pt pas, vce(cluster id)

. logit event d_2 d_3 d_4 d_5 d_6 pt pas, vce(cluster id)

Iteration 0: log pseudolikelihood = -352.11572 Iteration 1: log pseudolikelihood = -318.41917 Iteration 2: log pseudolikelihood = -314.6099 Iteration 3: log pseudolikelihood = -314.57352 Iteration 4: log pseudolikelihood = -314.57348 Iteration 5: log pseudolikelihood = -314.57348

Logistic regression Number of obs = 822 Wald chi2(7) = 70.30 Prob > chi2 = 0.0000Log pseudolikelihood = -314.57348 Pseudo R2 = 0.1066

(Std. Err. adjusted for 180 clusters in id)------------------------------------------------------------------------------ | Robust event | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- d_2 | -.6915223 .4725973 -1.46 0.143 -1.617796 .2347515 d_3 | .7430034 .3489 2.13 0.033 .0591719 1.426835 d_4 | 1.200057 .3421914 3.51 0.000 .5293743 1.87074 d_5 | 1.375541 .3546537 3.88 0.000 .6804328 2.07065 d_6 | 1.883353 .3588387 5.25 0.000 1.180042 2.586664 pt | .6605301 .2376159 2.78 0.005 .1948115 1.126249 pas | .2963606 .1198885 2.47 0.013 .0613835 .5313377 _cons | -2.893237 .3099899 -9.33 0.000 -3.500806 -2.285668------------------------------------------------------------------------------

. test d_2 d_3 d_4 d_5 d_6

( 1) [event]d_2 = 0 ( 2) [event]d_3 = 0 ( 3) [event]d_4 = 0 ( 4) [event]d_5 = 0

( 5) [event]d_6 = 0

chi2( 5) = 51.00 Prob > chi2 = 0.0000

. estat ic

Akaike's information criterion and Bayesian information criterion

----------------------------------------------------------------------------- Model | Obs ll(null) ll(model) df AIC BIC-------------+--------------------------------------------------------------- . | 822 -352.1157 -314.5735 8 645.147 682.8409----------------------------------------------------------------------------- Note: N=Obs used in calculating BIC; see [R] BIC note.

*Linear specification of time

. logit event period pt pas, vce(cluster id)

Iteration 0: log pseudolikelihood = -352.11572 Iteration 1: log pseudolikelihood = -320.9897 Iteration 2: log pseudolikelihood = -318.77263 Iteration 3: log pseudolikelihood = -318.76596 Iteration 4: log pseudolikelihood = -318.76596


(Std. Err. adjusted for 180 clusters in id)------------------------------------------------------------------------------ | Robust event | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- period | .4414011 .063851 6.91 0.000 .3162555 .5665468 pt | .6622977 .2368341 2.80 0.005 .1981113 1.126484 pas | .2966752 .1191258 2.49 0.013 .0631928 .5301575 _cons | -3.634963 .3164091 -11.49 0.000 -4.255114 -3.014813------------------------------------------------------------------------------

. estat ic



Quadratic specification of time

. gen periodsq=period^2

. logit event period periodsq pt pas, vce(cluster id)



(Std. Err. adjusted for 180 clusters in id)------------------------------------------------------------------------------ | Robust event | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- period | .4486464 .3357474 1.34 0.181 -.2094064 1.106699 periodsq | -.0010063 .04499 -0.02 0.982 -.0891851 .0871724 pt | .6621269 .2370769 2.79 0.005 .1974646 1.126789 pas | .2966369 .1192639 2.49 0.013 .0628839 .5303899 _cons | -3.645312 .5740438 -6.35 0.000 -4.770417 -2.520206------------------------------------------------------------------------------

* Cubic specification of time

. gen period3=period^3

. logit event period periodsq period3 pt pas, vce(cluster id)



(Std. Err. adjusted for 180 clusters in id)------------------------------------------------------------------------------ | Robust event | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- period | -.8741783 1.066687 -0.82 0.412 -2.964847 1.21649 periodsq | .4264202 .3297723 1.29 0.196 -.2199217 1.072762 period3 | -.0400951 .0306335 -1.31 0.191 -.1001356 .0199454 pt | .6653417 .2376999 2.80 0.005 .1994585 1.131225 pas | .2927601 .1196273 2.45 0.014 .058295 .5272252 _cons | -2.558018 1.00274 -2.55 0.011 -4.523352 -.592685------------------------------------------------------------------------------

. test period periodsq period3

( 1) [event]period = 0 ( 2) [event]periodsq = 0 ( 3) [event]period3 = 0

chi2( 3) = 54.04 Prob > chi2 = 0.0000

Complementary log log model linear

. cloglog event period pt pas, vce(cluster id)

Iteration 0: log pseudolikelihood = -320.49981 Iteration 1: log pseudolikelihood = -318.75042 Iteration 2: log pseudolikelihood = -318.74931 Iteration 3: log pseudolikelihood = -318.74931

Complementary log-log regression Number of obs = 822 Zero outcomes = 696 Nonzero outcomes = 126

Wald chi2(3) = 74.26Log pseudolikelihood = -318.74931 Prob > chi2 = 0.0000

(Std. Err. adjusted for 180 clusters in id)------------------------------------------------------------------------------ | Robust event | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- period | .3957886 .0559113 7.08 0.000 .2862045 .5053727 pt | .5961593 .2152241 2.77 0.006 .1743278 1.017991 pas | .2618993 .1022032 2.56 0.010 .0615847 .4622139 _cons | -3.543256 .2869683 -12.35 0.000 -4.105704 -2.980809------------------------------------------------------------------------------

. estat ic



Complementary log log model with time dummies

. cloglog event d_2 d_3 d_4 d_5 d_6 pt pas, vce(cluster id)




(Std. Err. adjusted for 180 clusters in id)------------------------------------------------------------------------------ | Robust event | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- d_2 | -.6748128 .4581414 -1.47 0.141 -1.572753 .2231279 d_3 | .669376 .3265844 2.05 0.040 .0292823 1.30947 d_4 | 1.083821 .3165693 3.42 0.001 .4633564 1.704285 d_5 | 1.237682 .3244006 3.82 0.000 .6018687 1.873496 d_6 | 1.681577 .3208799 5.24 0.000 1.052664 2.31049 pt | .5953676 .2155966 2.76 0.006 .172806 1.017929 pas | .2572451 .1024231 2.51 0.012 .0564994 .4579907 _cons | -2.876523 .2921512 -9.85 0.000 -3.449129 -2.303917------------------------------------------------------------------------------

. estat ic



. preserve

. cloglog event d_2 d_3 d_4 d_5 d_6 pt pas, vce(cluster id)




(Std. Err. adjusted for 180 clusters in id)------------------------------------------------------------------------------ | Robust event | Coef. Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- d_2 | -.6748128 .4581414 -1.47 0.141 -1.572753 .2231279 d_3 | .669376 .3265844 2.05 0.040 .0292823 1.30947 d_4 | 1.083821 .3165693 3.42 0.001 .4633564 1.704285 d_5 | 1.237682 .3244006 3.82 0.000 .6018687 1.873496 d_6 | 1.681577 .3208799 5.24 0.000 1.052664 2.31049 pt | .5953676 .2155966 2.76 0.006 .172806 1.017929 pas | .2572451 .1024231 2.51 0.012 .0564994 .4579907 _cons | -2.876523 .2921512 -9.85 0.000 -3.449129 -2.303917

Hazard ratio estimates

. cloglog, eform



(Std. Err. adjusted for 180 clusters in id)------------------------------------------------------------------------------ | Robust event | exp(b) Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- d_2 | .5092517 .2333093 -1.47 0.141 .2074731 1.24998 d_3 | 1.953018 .6378254 2.05 0.040 1.029715 3.704209 d_4 | 2.955952 .9357638 3.42 0.001 1.5894 5.497455 d_5 | 3.447613 1.118408 3.82 0.000 1.825527 6.511018 d_6 | 5.374026 1.724417 5.24 0.000 2.865275 10.07937 pt | 1.813698 .3910271 2.76 0.006 1.188635 2.767458 pas | 1.293362 .1324702 2.51 0.012 1.058126 1.580894 _cons | .0563303 .016457 -9.85 0.000 .0317733 .0998669------------------------------------------------------------------------------

. logistic event d_2 d_3 d_4 d_5 d_6 pt pas, vce(cluster id)


(Std. Err. adjusted for 180 clusters in id)------------------------------------------------------------------------------ | Robust event | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]-------------+---------------------------------------------------------------- d_2 | .5008131 .236683 -1.46 0.143 .1983353 1.264595 d_3 | 2.10224 .7334715 2.13 0.033 1.060958 4.165494 d_4 | 3.320307 1.13618 3.51 0.000 1.69787 6.4931 d_5 | 3.957218 1.403442 3.88 0.000 1.974732 7.929974 d_6 | 6.575517 2.35955 5.25 0.000 3.254512 13.28538 pt | 1.935818 .4599812 2.78 0.005 1.215082 3.084066 pas | 1.344955 .1612446 2.47 0.013 1.063307 1.701206 _cons | .0553966 .0171724 -9.33 0.000 .0301731 .1017061------------------------------------------------------------------------------

Hazard and odds ratio estimates close except at latest time periods.

expand dataset and the cloglog model version

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