我有一个看起来像这样的数据集：
Treatment Surface ex.time excision antib.time antibiotic inf.time infection
1 0 15 12 0 12 0 12 0
2 0 20 9 0 9 0 9 0
3 0 15 13 0 13 0 7 1
4 0 20 11 1 29 0 29 0
5 0 70 28 1 31 0 4 1
6 0 20 11 0 11 0 8 1
he variables represented in the dataset are as follows:
Observation number
Treatment
0-routine bathing 1-Body cleansing
Surface
Percentage of total surface area burned
Exis.time
Time to excision or on study time
Excision
indicator: 1=yes 0=no
Antib.time
Time to prophylactic antibiotic treatment or on study time
antibiotic
indicator: 1=yes 0=no
inf.time Time to straphylocous aureaus infection or on study time
infection
indicator: 1=yes 0=no
我想将感染前的时间建模为治疗、表面、抗生素治疗前的时间和切除前的时间。根据其他帖子，该数据集必须从宽转换为长。但是我不知道该怎么做？然后，一旦数据格式正确，我将使用以下公式：
coxph(Surv(start, stop, event) ~ m, data=times)
到目前为止，我只运行了一个正常的 Cox 回归，但我想这是不正确的，因为没有考虑时间依赖性？
coxph(formula = Surv(inf.time, infection) ~ Treatment + Surface +
ex.time + antib.time, data = BurnData)
n= 154, number of events= 48
coef exp(coef) se(coef) z Pr(>|z|)
Treatment -0.453748 0.635243 0.300805 -1.508 0.131
Surface 0.006932 1.006956 0.007333 0.945 0.345
ex.time 0.013503 1.013595 0.018841 0.717 0.474
antib.time 0.009546 1.009592 0.009560 0.999 0.318
exp(coef) exp(-coef) lower .95 upper .95
Treatment 0.6352 1.5742 0.3523 1.145
Surface 1.0070 0.9931 0.9926 1.022
ex.time 1.0136 0.9866 0.9768 1.052
antib.time 1.0096 0.9905 0.9909 1.029
Concordance= 0.576 (se = 0.046 )
Rsquare= 0.041 (max possible= 0.942 )
Likelihood ratio test= 6.5 on 4 df, p=0.1648
Wald test = 6.55 on 4 df, p=0.1618
Score (logrank) test = 6.71 on 4 df, p=0.1519