How to include the interaction between a covariate and time for a non-proportional hazards model?
How to include the interaction between a covariate and and time for a non-proportional hazards model?
I often find that the proportional hazards assumption for the Cox regressions doesn’t hold.
Take the following data as an example.
> head(data2)
no np_p age_dx1 race1 mr_dx er_1 pr_1 sct_1 surv_mo km_stts1
1 20 1 2 4 1 2 2 4 52 1
2 33 1 3 1 2 1 2 1 11 1
3 67 1 2 4 4 1 1 3 20 1
4 90 1 3 1 3 3 3 2 11 1
5 143 1 2 4 3 1 1 2 123 0
6 180 1 3 1 3 1 1 2 9 1
First, I fitted a Cox regression model.
> fit2 <- coxph(Surv(surv_mo, km_stts1) ~ np_p + age_dx1 + race1 + mr_dx + er_1 + pr_1 + sct_1, data = data)
Second, I assessed the proportional hazards assumption.
> check_PH2 <- cox.zph(fit2, transform = "km")
> check_PH2
rho chisq p
np_p 0.00946 0.0748 7.84e-01
age_dx1 -0.00889 0.0640 8.00e-01
race1 -0.03148 0.7827 3.76e-01
mr_dx -0.03120 0.7607 3.83e-01
er_1 -0.14741 18.5972 1.61e-05
pr_1 0.05906 2.9330 8.68e-02
sct_1 0.17651 23.8030 1.07e-06
GLOBAL NA 53.2844 3.26e-09
So, this means that the hazard function of er_1 and sct_1 were nonproportional over time (Right?).
In my opinion, I can include the interaction between these two covariates and time seperately in the model. But I don't know how to perform it using R.
Thank you.
r cox-regression
asked Dec 31 '18 at 9:25
Lin CaijinLin Caijin
12
add a comment |
How to include the interaction between a covariate and and time for a non-proportional hazards model?
I often find that the proportional hazards assumption for the Cox regressions doesn’t hold.
Take the following data as an example.
> head(data2)
no np_p age_dx1 race1 mr_dx er_1 pr_1 sct_1 surv_mo km_stts1
1 20 1 2 4 1 2 2 4 52 1
2 33 1 3 1 2 1 2 1 11 1
3 67 1 2 4 4 1 1 3 20 1
4 90 1 3 1 3 3 3 2 11 1
5 143 1 2 4 3 1 1 2 123 0
6 180 1 3 1 3 1 1 2 9 1
First, I fitted a Cox regression model.
> fit2 <- coxph(Surv(surv_mo, km_stts1) ~ np_p + age_dx1 + race1 + mr_dx + er_1 + pr_1 + sct_1, data = data)
Second, I assessed the proportional hazards assumption.
> check_PH2 <- cox.zph(fit2, transform = "km")
> check_PH2
rho chisq p
np_p 0.00946 0.0748 7.84e-01
age_dx1 -0.00889 0.0640 8.00e-01
race1 -0.03148 0.7827 3.76e-01
mr_dx -0.03120 0.7607 3.83e-01
er_1 -0.14741 18.5972 1.61e-05
pr_1 0.05906 2.9330 8.68e-02
sct_1 0.17651 23.8030 1.07e-06
GLOBAL NA 53.2844 3.26e-09
So, this means that the hazard function of er_1 and sct_1 were nonproportional over time (Right?).
In my opinion, I can include the interaction between these two covariates and time seperately in the model. But I don't know how to perform it using R.
Thank you.
r cox-regression
asked Dec 31 '18 at 9:25
Lin CaijinLin Caijin
12
In R, an interaction is denoted using a*symbol. Is this what you're after?
– Roman Luštrik
Dec 31 '18 at 10:49
@RomanLuštrik Thank you for your answer. In other words, I want to know how to deal with the non-proportionality in the Cox regression model when the assumption is not met. I've read many papers including interactions between time and the covariate in this situation.
– Lin Caijin
Dec 31 '18 at 12:08
add a comment |
How to include the interaction between a covariate and and time for a non-proportional hazards model?
I often find that the proportional hazards assumption for the Cox regressions doesn’t hold.
Take the following data as an example.
> head(data2)
no np_p age_dx1 race1 mr_dx er_1 pr_1 sct_1 surv_mo km_stts1
1 20 1 2 4 1 2 2 4 52 1
2 33 1 3 1 2 1 2 1 11 1
3 67 1 2 4 4 1 1 3 20 1
4 90 1 3 1 3 3 3 2 11 1
5 143 1 2 4 3 1 1 2 123 0
6 180 1 3 1 3 1 1 2 9 1
First, I fitted a Cox regression model.
> fit2 <- coxph(Surv(surv_mo, km_stts1) ~ np_p + age_dx1 + race1 + mr_dx + er_1 + pr_1 + sct_1, data = data)
Second, I assessed the proportional hazards assumption.
> check_PH2 <- cox.zph(fit2, transform = "km")
> check_PH2
rho chisq p
np_p 0.00946 0.0748 7.84e-01
age_dx1 -0.00889 0.0640 8.00e-01
race1 -0.03148 0.7827 3.76e-01
mr_dx -0.03120 0.7607 3.83e-01
er_1 -0.14741 18.5972 1.61e-05
pr_1 0.05906 2.9330 8.68e-02
sct_1 0.17651 23.8030 1.07e-06
GLOBAL NA 53.2844 3.26e-09
So, this means that the hazard function of er_1 and sct_1 were nonproportional over time (Right?).
In my opinion, I can include the interaction between these two covariates and time seperately in the model. But I don't know how to perform it using R.
Thank you.
r cox-regression
asked Dec 31 '18 at 9:25
Lin CaijinLin Caijin
12
How to include the interaction between a covariate and and time for a non-proportional hazards model?
I often find that the proportional hazards assumption for the Cox regressions doesn’t hold.
Take the following data as an example.
> head(data2)
no np_p age_dx1 race1 mr_dx er_1 pr_1 sct_1 surv_mo km_stts1
1 20 1 2 4 1 2 2 4 52 1
2 33 1 3 1 2 1 2 1 11 1
3 67 1 2 4 4 1 1 3 20 1
4 90 1 3 1 3 3 3 2 11 1
5 143 1 2 4 3 1 1 2 123 0
6 180 1 3 1 3 1 1 2 9 1
First, I fitted a Cox regression model.
> fit2 <- coxph(Surv(surv_mo, km_stts1) ~ np_p + age_dx1 + race1 + mr_dx + er_1 + pr_1 + sct_1, data = data)
Second, I assessed the proportional hazards assumption.
> check_PH2 <- cox.zph(fit2, transform = "km")
> check_PH2
rho chisq p
np_p 0.00946 0.0748 7.84e-01
age_dx1 -0.00889 0.0640 8.00e-01
race1 -0.03148 0.7827 3.76e-01
mr_dx -0.03120 0.7607 3.83e-01
er_1 -0.14741 18.5972 1.61e-05
pr_1 0.05906 2.9330 8.68e-02
sct_1 0.17651 23.8030 1.07e-06
GLOBAL NA 53.2844 3.26e-09
So, this means that the hazard function of er_1 and sct_1 were nonproportional over time (Right?).
In my opinion, I can include the interaction between these two covariates and time seperately in the model. But I don't know how to perform it using R.
Thank you.
r cox-regression
r cox-regression
asked Dec 31 '18 at 9:25
Lin CaijinLin Caijin
12
asked Dec 31 '18 at 9:25
Lin CaijinLin Caijin
12
asked Dec 31 '18 at 9:25
Lin CaijinLin Caijin
12
asked Dec 31 '18 at 9:25
Lin CaijinLin Caijin
12
asked Dec 31 '18 at 9:25
Lin CaijinLin Caijin
12
12
In R, an interaction is denoted using a*symbol. Is this what you're after?
– Roman Luštrik
Dec 31 '18 at 10:49
@RomanLuštrik Thank you for your answer. In other words, I want to know how to deal with the non-proportionality in the Cox regression model when the assumption is not met. I've read many papers including interactions between time and the covariate in this situation.
– Lin Caijin
Dec 31 '18 at 12:08
add a comment |
In R, an interaction is denoted using a*symbol. Is this what you're after?
– Roman Luštrik
Dec 31 '18 at 10:49
@RomanLuštrik Thank you for your answer. In other words, I want to know how to deal with the non-proportionality in the Cox regression model when the assumption is not met. I've read many papers including interactions between time and the covariate in this situation.
– Lin Caijin
Dec 31 '18 at 12:08
In R, an interaction is denoted using a
* symbol. Is this what you're after?– Roman Luštrik
Dec 31 '18 at 10:49
In R, an interaction is denoted using a
* symbol. Is this what you're after?– Roman Luštrik
Dec 31 '18 at 10:49
@RomanLuštrik Thank you for your answer. In other words, I want to know how to deal with the non-proportionality in the Cox regression model when the assumption is not met. I've read many papers including interactions between time and the covariate in this situation.
– Lin Caijin
Dec 31 '18 at 12:08
@RomanLuštrik Thank you for your answer. In other words, I want to know how to deal with the non-proportionality in the Cox regression model when the assumption is not met. I've read many papers including interactions between time and the covariate in this situation.
– Lin Caijin
Dec 31 '18 at 12:08
add a comment |
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In R, an interaction is denoted using a
*symbol. Is this what you're after?– Roman Luštrik
Dec 31 '18 at 10:49
@RomanLuštrik Thank you for your answer. In other words, I want to know how to deal with the non-proportionality in the Cox regression model when the assumption is not met. I've read many papers including interactions between time and the covariate in this situation.
– Lin Caijin
Dec 31 '18 at 12:08