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The data files used for the examples in this text can be downloaded in a zip file from the Wiley FTP website or the Stata Web site. You can then use a program such as WinZip to unzip the data files. If you need assistance getting data into Stata, please see our Stata Class Notes, especially the unit on Entering Data. (NOTE: The *.dat files are the data files, and the *.txt files contain the codebook information.)
use hmohiv, clear stset time, failure(censor)
Table 8.1, page 278.
streg drug, dist(exp) time
Exponential regression -- accelerated failure-time form
No. of subjects = 100 Number of obs = 100
No. of failures = 80
Time at risk = 1136
LR chi2(1) = 20.93
Log likelihood = -146.79209 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
_t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
drug | -1.055687 .2238868 -4.72 0.000 -1.494497 -.6168771
_cons | 3.023903 .1543033 19.60 0.000 2.721474 3.326332
------------------------------------------------------------------------------
Table 8.2, page 280.
streg age drug, dist(exp) time
Exponential regression -- accelerated failure-time form
No. of subjects = 100 Number of obs = 100
No. of failures = 80
Time at risk = 1136
LR chi2(2) = 53.72
Log likelihood = -130.39708 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
_t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | -.0920916 .0161799 -5.69 0.000 -.1238037 -.0603795
drug | -1.009856 .2239834 -4.51 0.000 -1.448855 -.5708561
_cons | 6.151631 .6061974 10.15 0.000 4.963506 7.339756
------------------------------------------------------------------------------
Figure 8.1, page 282.
predict mgale, mgale mat V = e(V) generate l1 = -age*mgale generate l2 = -drug*mgale generate l3 = -1*mgale mkmat l1 l2 l3, mat(L) matrix DB = L*V svmat DB, name(db) graph twoway scatter db1 age
Figure 8.2, page 284.
graph box db2, over(drug)
Figure 8.3, page 285.
generate ld = l1*db1+l2*dbs+l3*db3 generate label = censor graph twoway scatter ld mgale , mlabel(label) msymbol(i)
Table 8.4, page 293.
streg age drug, dist(weib) time
Weibull regression -- accelerated failure-time form
No. of subjects = 100 Number of obs = 100
No. of failures = 80
Time at risk = 1136
LR chi2(2) = 52.05
Log likelihood = -128.50229 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
_t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | -.0907665 .013616 -6.67 0.000 -.1174534 -.0640796
drug | -1.049168 .1889778 -5.55 0.000 -1.419558 -.6787786
_cons | 6.14794 .5107206 12.04 0.000 5.146946 7.148934
-------------+----------------------------------------------------------------
/ln_p | .1750802 .0860646 2.03 0.042 .0063967 .3437637
-------------+----------------------------------------------------------------
p | 1.191342 .1025323 1.006417 1.410245
1/p | .8393897 .0722417 .7090965 .9936237
------------------------------------------------------------------------------
Figure 8.5a, page 296.
drop mgale-ld predict mgale, mgale mat V = e(V) generate l1 = -age*mgale generate l2 = -drug*mgale generate l3 = -1*mgale generate l4 = -1*mgale mkmat l1 l2 l3 l4, mat(L) matrix DB = L*V svmat DB, name(db) graph twoway scatter db1 age
Figure 8.5b, page 296.
graph box db2, over(drug)
Table 8.5, page 302.
streg age drug, dist(llog) time
Log-logistic regression -- accelerated failure-time form
No. of subjects = 100 Number of obs = 100
No. of failures = 80
Time at risk = 1136
LR chi2(2) = 39.07
Log likelihood = -129.10606 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
_t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | -.0873809 .0154681 -5.65 0.000 -.1176979 -.057064
drug | -.8914631 .2135028 -4.18 0.000 -1.309921 -.4730054
_cons | 5.539507 .5750993 9.63 0.000 4.412333 6.666681
-------------+----------------------------------------------------------------
/ln_gam | -.5305458 .0921406 -5.76 0.000 -.711138 -.3499535
-------------+----------------------------------------------------------------
gamma | .5882838 .0542048 .491085 .7047208
------------------------------------------------------------------------------
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