I have a problem regarding the difference in the number of observations between the Cox model and the frequency analysis by year.
Here, I have a data set of the 2009 to 2019 wave (year).
Year | Freq. | Percent | Cum. |
2009 | 104 | 11.30 | 11.30 |
2010 | 95 | 10.33 | 21.63 |
2011 | 95 | 10.33 | 31.96 |
2012 | 91 | 9.89 | 41.85 |
2013 | 86 | 9.35 | 51.20 |
2014 | 81 | 8.80 | 60.00 |
2015 | 77 | 8.37 | 68.37 |
2016 | 76 | 8.26 | 76.63 |
2017 | 74 | 8.04 | 84.67 |
2018 | 72 | 7.83 | 92.50 |
2019 | 69 | 7.50 | 100.00 |
Total | 920 | 100.00 |
However, when I run the Cox model the number of observations is bigger.
Can somebody explain why this could happen?
The dependant variable is the exit of medical benefit (coded as medical_final)
medical_final | |||
Freq. | Percent | Cum. | |
0 | 675 | 73.37 | 73.37 |
1 | 245 | 26.63 | 100.00 |
Total | 920 | 100.00 |
. stset tf, failure(medical_final=1) | ||
failure event: medical_final == 1 | ||
obs. time interval: (0, tf] | ||
exit on or before: failure | ||
920 total observations | ||
0 exclusions | ||
920 observations remaining, representing | ||
245 failures in single-record/single-failure data | ||
5,151 total analysis time at risk and under observation | ||
at risk from t | = | 0 |
earliest observed entry t | = | 0 |
last observed exit t | = | 11 |
. |
failure _d: medical_final == 1
analysis time _t: tf
Iteration 0: log likelihood = -331.49314
Iteration 1: log likelihood = -314.58647
Iteration 2: log likelihood = -312.79623
Iteration 3: log likelihood = -312.78731
Iteration 4: log likelihood = -312.78731
Refining estimates:
Iteration 0: log likelihood = -312.78731
Cox regression -- Breslow method for ties
No. of subjects = 296 Number of obs = 296
No. of failures = 77
Time at risk = 1473
LR chi2(17) = 37.41
Log likelihood = -312.78731 Prob > chi2 = 0.0030
_t | Coef. | Std. Err. | z | P>z | [95% Conf. | Interval] |
sex | ||||||
women | -.5941747 | .4756698 | -1.25 | 0.212 | -1.52647 | .3381209 |
age | -.0787584 | .0431815 | -1.82 | 0.068 | -.1633926 | .0058758 |
1.married | -.3554892 | .5308085 | -0.67 | 0.503 | -1.395855 | .6848763 |
householdmembers | .1617833 | .1848749 | 0.88 | 0.382 | -.2005648 | .5241314 |
yearsofshooling | .0038488 | .0603173 | 0.06 | 0.949 | -.114371 | .1220686 |
chronic_disease | ||||||
1 | -.2000307 | 1.044555 | -0.19 | 0.848 | -2.247322 | 1.84726 |
2 | -.6337595 | 1.04773 | -0.60 | 0.545 | -2.687273 | 1.419754 |
3 | -.3988995 | .2983077 | -1.34 | 0.181 | -.9835718 | .1857728 |
1.have_disability | .2235872 | .3358214 | 0.67 | 0.506 | -.4346106 | .8817849 |
alcoholqualtity | .0866133 | .0932219 | 0.93 | 0.353 | -.0960983 | .2693248 |
depression | -.1622675 | .2658865 | -0.61 | 0.542 | -.6833954 | .3588604 |
jobtype | ||||||
2 | -1.194072 | .5009966 | -2.38 | 0.017 | -2.176007 | -.2121367 |
3 | -1.248776 | .5771651 | -2.16 | 0.030 | -2.379999 | -.1175533 |
4 | -2.392911 | .667759 | -3.58 | 0.000 | -3.701694 | -1.084127 |
1.jobstable | -.5512276 | .3898746 | -1.41 | 0.157 | -1.315368 | .2129126 |
1.fulltimejob | .1312842 | .2975419 | 0.44 | 0.659 | -.4518872 | .7144557 |
totalcostofliving | -.0011913 | .0012785 | -0.93 | 0.351 | -.0036972 | .0013145 |
0 Response to Difference in N of obs. in Cox model
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