Dear Experts
I would greatly appreciate if you could let me know how to do discrete time survival analysis with time varying covariates.
Some part of my data set is as follows (d1-d12: are dummy variables for each time period):
Non-parametric methods:
. logit event d1 d2 d3 d4 d5 d6 d7 d8 d9 d10 d11 d12 x1 x2 x3 x4 x5, cluster(id) nocons
. logit event ln(time) x1 x2 x3 x4 x5, cluster(id) nocons
. cloglog event d1 d2 d3 d4 d5 d6 d7 d8 d9 d10 d11 d12 x1 x2 x3 x4 x5, cluster(id) nocons
. cloglog event ln(time) x1 x2 x3 x4 x5, cluster(id) nocons
. xtset id time, yearly
. xtlogit event x1 x2 x3 x4 x5, pa corr(exchangeable)
. xtlogit event x1 x2 x3 x4 x5, re
. xtlogit event x1 x2 x3 x4 x5, fe
. xtcloglog event x1 x2 x3 x4 x5, re
. xtcloglog event x1 x2 x3 x4 x5, pa corr(exchangeable)
Semi-parametric method:
. stset time, failure(event==1)
. stcox x1 x2 x3 x4 x5, cluster(id)
. stcox x1 x2 x3 x4 x5
. stcox x1 x2 x3 x4 x5, shared(id)
Parametric methods:
. xtset id time, yearly
. stset time, failure(event==1)
. xtstreg x1 x2 x3 x4 x5, dist(exponential)
. xtstreg x1 x2 x3 x4 x5, dist(loglogistic)
. xtstreg x1 x2 x3 x4 x5, dist(lognormal)
. xtstreg x1 x2 x3 x4 x5, dist(weibull)
. xtstreg x1 x2 x3 x4 x5, dist(gama)
. stset time, failure(event)
. mestreg x1 x2 x3 x4 x5, dist(exponential)
. mestreg x1 x2 x3 x4 x5, dist(loglogistic)
. mestreg x1 x2 x3 x4 x5, dist(lognormal)
. mestreg x1 x2 x3 x4 x5, dist(weibull)
. mestreg x1 x2 x3 x4 x5, dist(gama)
Thanks in advance.
Best regards,
I would greatly appreciate if you could let me know how to do discrete time survival analysis with time varying covariates.
Some part of my data set is as follows (d1-d12: are dummy variables for each time period):
ID | TIME | EVENT | x1 | x2 | x3 | x4 | x5 |
1 | 1 | 0 | 1.28 | 0.02 | 0.87 | 1.22 | 0.06 |
1 | 2 | 0 | 1.27 | 0.01 | 0.82 | 1.00 | -0.01 |
1 | 3 | 0 | 1.05 | -0.06 | 0.92 | 0.73 | 0.02 |
1 | 4 | 0 | 1.11 | -0.02 | 0.86 | 0.81 | 0.08 |
1 | 5 | 1 | 1.22 | -0.06 | 0.89 | 0.48 | 0.01 |
2 | 1 | 0 | 1.06 | 0.11 | 0.81 | 0.84 | 0.20 |
2 | 2 | 0 | 1.06 | 0.08 | 0.88 | 0.69 | 0.14 |
2 | 3 | 0 | 0.97 | 0.08 | 0.91 | 0.81 | 0.17 |
2 | 4 | 0 | 1.06 | 0.13 | 0.82 | 0.88 | 0.23 |
2 | 5 | 0 | 1.12 | 0.15 | 0.76 | 1.08 | 0.28 |
2 | 6 | 0 | 1.60 | 0.26 | 0.55 | 1.31 | 0.37 |
2 | 7 | 0 | 1.58 | 0.26 | 0.56 | 1.16 | 0.35 |
2 | 8 | 0 | 1.54 | 0.24 | 0.59 | 1.08 | 0.33 |
2 | 9 | 0 | 1.72 | 0.22 | 0.55 | 0.84 | 0.29 |
2 | 10 | 0 | 1.72 | 0.21 | 0.53 | 0.79 | 0.29 |
2 | 11 | 0 | 1.63 | 0.19 | 0.55 | 0.73 | 0.27 |
2 | 12 | 0 | 2.17 | 0.32 | 0.44 | 0.95 | 0.43 |
3 | 1 | 0 | 0.87 | -0.03 | 0.79 | 0.61 | 0.00 |
3 | 2 | 1 | 0.83 | -0.14 | 0.95 | 0.57 | -0.02 |
Non-parametric methods:
. logit event d1 d2 d3 d4 d5 d6 d7 d8 d9 d10 d11 d12 x1 x2 x3 x4 x5, cluster(id) nocons
. logit event ln(time) x1 x2 x3 x4 x5, cluster(id) nocons
. cloglog event d1 d2 d3 d4 d5 d6 d7 d8 d9 d10 d11 d12 x1 x2 x3 x4 x5, cluster(id) nocons
. cloglog event ln(time) x1 x2 x3 x4 x5, cluster(id) nocons
. xtset id time, yearly
. xtlogit event x1 x2 x3 x4 x5, pa corr(exchangeable)
. xtlogit event x1 x2 x3 x4 x5, re
. xtlogit event x1 x2 x3 x4 x5, fe
. xtcloglog event x1 x2 x3 x4 x5, re
. xtcloglog event x1 x2 x3 x4 x5, pa corr(exchangeable)
Semi-parametric method:
. stset time, failure(event==1)
. stcox x1 x2 x3 x4 x5, cluster(id)
. stcox x1 x2 x3 x4 x5
. stcox x1 x2 x3 x4 x5, shared(id)
Parametric methods:
. xtset id time, yearly
. stset time, failure(event==1)
. xtstreg x1 x2 x3 x4 x5, dist(exponential)
. xtstreg x1 x2 x3 x4 x5, dist(loglogistic)
. xtstreg x1 x2 x3 x4 x5, dist(lognormal)
. xtstreg x1 x2 x3 x4 x5, dist(weibull)
. xtstreg x1 x2 x3 x4 x5, dist(gama)
. stset time, failure(event)
. mestreg x1 x2 x3 x4 x5, dist(exponential)
. mestreg x1 x2 x3 x4 x5, dist(loglogistic)
. mestreg x1 x2 x3 x4 x5, dist(lognormal)
. mestreg x1 x2 x3 x4 x5, dist(weibull)
. mestreg x1 x2 x3 x4 x5, dist(gama)
Thanks in advance.
Best regards,
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