CODEWITHSUNDEEP
rsurvival-analysiscovariant
tianbu
tianbu

Reputation: 11

plot survival curve after adjusting for gender

I have a dataset that have samples with / without treatment and their ages at death and gender. All the samples are dead. I want to test if the treatment affects the survival.

The dataset df looks like below

  FID gender age_at_death treatment event
1 101 female      46  Y     1
2 102 female      65  Y     1
3 103   male      73  Y     1
4 104   male      74  N     1
5 105 female      56  N     1
6 106   male      57  N     1

Below is my code to test if the treatment affects survival: 

library(survminer)    
surv_obj <- Surv(time=df$age_at_death, event=df$event)
fit <- survfit(surv_obj ~treatment, data=df)
ggsurvplot(fit, data = df, pval = TRUE, title = "test" )

surv plot for different treatment group

enter image description here

But gender is quite an important co variant (females usually live longer than males), therefore I want to adjust for gender. But the below code give me 4 survival curves. What I want is two curves (treated vs non-treat) adjusted for gender. 

fit1 <- survfit(surv_obj ~treatment + gender, data=df)
ggsurvplot(fit, data = df, pval = TRUE, title = "test" )

enter image description here

fit2 <- coxph( Surv(time=df$age_at_death, event=df$event) ~ treatment, data = df )
ggadjustedcurves(fit2, data = df)

This only give one curve.

enter image description here

fit3 <- coxph( Surv(time=df$age_at_death, event=df$event) ~ treatment +strata(gender), data = df )
ggadjustedcurves(fit3, data = df)

This gives twos curve, male vs female.

enter image description here

The figure I want is similar to this example:

enter image description here

"after adjustment for age, sex, race, diseases suspected to influence B27 testing and mortality". They adjusted for quite a few covariants and have an adjusted survival plot for B27+ and B27- (mine is treated vs non-treated).

Any help will be appreciated!!!

Upvotes: 1

Views: 739

Answers (1)

Denzo
Denzo

Reputation: 355

I would suggest taking a look at the adjustedCurves package: https://github.com/RobinDenz1/adjustedCurves

It offers a variety of ways to adjust survival curves for confounders. The literature references given in the documentation of that package can also be used to get a good understanding of what exactly confounder-adjustment means in this context and how it is performed.

Upvotes: 0

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