CODEWITHSUNDEEP
rconfidence-intervalcox-regression
Daniel
Daniel

Reputation: 97

How to combine hazard ratios and confidence intervals from Cox regression analyses in R

I have performed a Cox regression analysis including four variables (sex, age and two binary explanatory variables) which all have significant associations to outcome. I have used the coxph function from the "survival" package in R:

    library(survival)
cox <- coxph(Surv(time, status_risk==1) ~ sex + age + stone_number +stone_size, data=cox_cut)
    summary(cox1_3_cut)
Call:
coxph(formula = Surv(time, status_risk == 1) ~ sex + age + 
stone_number + stone_size, data = cox_cut)

n= 582, number of events= 48 
(82 observations deleted due to missingness)

                      coef exp(coef) se(coef)      z Pr(>|z|)    
sexfemale              0.76993   2.15961  0.34577  2.227 0.025966 *  
age                   -0.03222   0.96829  0.01201 -2.682 0.007311 ** 
stone_number>=2        0.60646   1.83393  0.29942  2.025 0.042821 *  
stone_size>10          1.02593   2.78969  0.29391  3.491 0.000482 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                  exp(coef) exp(-coef) lower .95 upper .95
sexfemale                2.1596     0.4630    1.0966    4.2530
age                      0.9683     1.0327    0.9458    0.9914
stone_number>=2          1.8339     0.5453    1.0198    3.2980
stone_size>10            2.7897     0.3585    1.5681    4.9629

I would like to make a prediction score table including the four variables with 4 age stratified Groups (30, 40, 50, 60 years of age). All hazards in this table must be divided by one predefined hazard in order to obtain HR for each age group.

How to compute the HR with the 95% CI for each of these specific age-groups in R?

Upvotes: 0

Views: 2404

Answers (1)

Jthorpe
Jthorpe

Reputation: 10167

As per @shadow's comment, the CI of the parameter estimate is based on the whole dataset, if you want age conditional CI's you need to subset your data. If instead you want to generate expected survival curves conditional on a set of covariates (including age) this is how you do it: 

# Create a dummy dataset
df <- data.frame(sex = sample(c(T,F),100,T),
                 age = 50 + rnorm(100)*10,
                 foo = sample(c('a','b','c'),100,T),
                 bar = sample(c('x','y','z'),100,T),
                 status = sample(c(T,F),100,T),
                 start = 0,# required for the survfit with `individual = TRUE`
                 time = -log(runif(100)))

# fit the A coxph model in your full dataset data
cox <- coxph(Surv(start,time, status) ~ sex + age + foo + bar, data=df)


# create a data.frame with all the variables used in the formula
newData <- data.frame(sex = T,
                 age = 55,
                 foo = sample(c('a','b','c'),1),
                 bar = sample(c('x','y','z'),1),
                 status = T,# required but unused
                 start = 0,# required but unused
                 time = 1)# required but unused
# get a prediction from the fitted model, specifiying 'individual = TRUE'
pred  <-  survfit(cox, newdata=data.frame(newData),individual =TRUE)

# plot the survival curves 
matplot(x = cbind(pred$"time"),
        y = cbind(pred$surv,
                  pred$upper,
                  pred$lower),
        type = 'l',
        lty= c(1,2,2),
        main = 'Predicted Survial with 95% CI')

You may also wnat to inspect unclass(pred) and summary(pred).

Upvotes: 1

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