Tidy a(n) coxph object

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies cross models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

# S3 method for coxph
tidy(x, exponentiate = FALSE, conf.int = TRUE,
  conf.level = 0.95, ...)

Arguments

x	A coxph object returned from survival::coxph().
exponentiate	Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to FALSE.
conf.int	Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.
conf.level	The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.
...	Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

Value

A tibble::tibble with one row for each term and columns:

estimate

estimate of slope

std.error

standard error of estimate

statistic

test statistic

p.value

p-value

See also

tidy(), survival::coxph()

Other coxph tidiers: augment.coxph, glance.coxph

Other survival tidiers: augment.coxph, augment.survreg, glance.aareg, glance.cch, glance.coxph, glance.pyears, glance.survdiff, glance.survexp, glance.survfit, glance.survreg, tidy.aareg, tidy.cch, tidy.pyears, tidy.survdiff, tidy.survexp, tidy.survfit, tidy.survreg

Examples

library(survival)

cfit <- coxph(Surv(time, status) ~ age + sex, lung)

tidy(cfit)
#> # A tibble: 2 x 7
#>   term  estimate std.error statistic p.value conf.low conf.high
#>   <chr>    <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
#> 1 age     0.0170   0.00922      1.85 0.0646  -0.00103    0.0351
#> 2 sex    -0.513    0.167       -3.06 0.00218 -0.841     -0.185 
tidy(cfit, exponentiate = TRUE)
#> # A tibble: 2 x 7
#>   term  estimate std.error statistic p.value conf.low conf.high
#>   <chr>    <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
#> 1 age      1.02    0.00922      1.85 0.0646     0.999     1.04 
#> 2 sex      0.599   0.167       -3.06 0.00218    0.431     0.831

lp <- augment(cfit, lung)
risks <- augment(cfit, lung, type.predict = "risk")
expected <- augment(cfit, lung, type.predict = "expected")

glance(cfit)
#> # A tibble: 1 x 15
#>       n nevent statistic.log p.value.log statistic.sc p.value.sc statistic.wald
#>   <int>  <dbl>         <dbl>       <dbl>        <dbl>      <dbl>          <dbl>
#> 1   228    165          14.1    0.000857         13.7    0.00105           13.5
#> # … with 8 more variables: p.value.wald <dbl>, r.squared <dbl>,
#> #   r.squared.max <dbl>, concordance <dbl>, std.error.concordance <dbl>,
#> #   logLik <dbl>, AIC <dbl>, BIC <dbl>

# also works on clogit models
resp <- levels(logan$occupation)
n <- nrow(logan)
indx <- rep(1:n, length(resp))
logan2 <- data.frame(
  logan[indx,],
  id = indx,
  tocc = factor(rep(resp, each=n))
)

logan2$case <- (logan2$occupation == logan2$tocc)

cl <- clogit(case ~ tocc + tocc:education + strata(id), logan2)
tidy(cl)
#> # A tibble: 9 x 7
#>   term                 estimate std.error statistic   p.value conf.low conf.high
#>   <chr>                   <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl>
#> 1 toccfarm               -1.90     1.38       -1.37  1.70e- 1  -4.60       0.810
#> 2 toccoperatives          1.17     0.566       2.06  3.91e- 2   0.0581     2.28 
#> 3 toccprofessional       -8.10     0.699     -11.6   4.45e-31  -9.47      -6.73 
#> 4 toccsales              -5.03     0.770      -6.53  6.54e-11  -6.54      -3.52 
#> 5 tocccraftsmen:educa…   -0.332    0.0569     -5.84  5.13e- 9  -0.444     -0.221
#> 6 toccfarm:education     -0.370    0.116      -3.18  1.47e- 3  -0.598     -0.142
#> 7 toccoperatives:educ…   -0.422    0.0584     -7.23  4.98e-13  -0.537     -0.308
#> 8 toccprofessional:ed…    0.278    0.0510      5.45  4.94e- 8   0.178      0.378
#> 9 toccsales:education    NA        0          NA    NA         NA         NA    
glance(cl)
#> # A tibble: 1 x 15
#>       n nevent statistic.log p.value.log statistic.sc p.value.sc statistic.wald
#>   <int>  <dbl>         <dbl>       <dbl>        <dbl>      <dbl>          <dbl>
#> 1  4190    838          666.   1.90e-138         682.  5.01e-142           414.
#> # … with 8 more variables: p.value.wald <dbl>, r.squared <dbl>,
#> #   r.squared.max <dbl>, concordance <dbl>, std.error.concordance <dbl>,
#> #   logLik <dbl>, AIC <dbl>, BIC <dbl>

library(ggplot2)

ggplot(lp, aes(age, .fitted, color = sex)) +
  geom_point()

ggplot(risks, aes(age, .fitted, color = sex)) +
  geom_point()

ggplot(expected, aes(time, .fitted, color = sex)) +
  geom_point()