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Regression tables for clinical variables

Source: R/models.R
cox_table.Rd

cox_table() and logistic_table() fit each variable on its own, fit one multivariable model, and return a single tidy table with the univariable and multivariable estimates side by side. Numeric variables contribute one row with a per-unit estimate; factors contribute one row per level against the reference level.

Usage

cox_table(
  data,
  time,
  event,
  factors = NULL,
  max_time = NULL,
  multivariable = c("all", "significant", "none"),
  significance = 0.05,
  ci_level = 0.95
)

logistic_table(
  data,
  outcome,
  factors = NULL,
  positive = NULL,
  multivariable = c("all", "significant", "none"),
  significance = 0.05,
  ci_level = 0.95
)

Arguments

data

Data frame holding the variables.

time, event

Column names of the follow-up time and event indicator used by cox_table().

factors

Character vector of variable names to analyse. Defaults to every column of data except the time and event columns.

max_time

Optional follow-up limit for cox_table(). Times beyond the limit are censored at the limit.

multivariable

Which variables enter the multivariable model: "all" (default), "significant", or "none".

significance

Significance level used by multivariable = "significant".

ci_level

Confidence level of the reported intervals.

outcome

Column name of the binary outcome used by logistic_table().

positive

Value of outcome that represents the event.

Value

A tibble with one row per term and the columns term, variable, level, n, n_event, the univariable estimate (hr_univariable or or_univariable) with interval and p-value, and the corresponding multivariable columns.

Details

The multivariable model includes all requested variables by default. With multivariable = "significant" only variables whose univariable likelihood-ratio test is below significance are included, which reproduces the screening rule of the original helper functions.

Examples

set.seed(42)
dat <- data.frame(
  time = rexp(60, 0.1),
  event = rbinom(60, 1, 0.7),
  age = rnorm(60, 60, 8),
  stage = factor(sample(c("II", "III"), 60, replace = TRUE))
)
cox_table(dat, time = "time", event = "event")
#> # A tibble: 2 × 13
#>   term   variable level     n n_event hr_univariable univ_ci_lower univ_ci_upper
#>   <chr>  <chr>    <chr> <int>   <int>          <dbl>         <dbl>         <dbl>
#> 1 age    age      NA       60      36          1.00          0.961          1.05
#> 2 stage… stage    III      60      36          0.968         0.495          1.89
#> # ℹ 5 more variables: univ_p <dbl>, hr_multivariable <dbl>,
#> #   multi_ci_lower <dbl>, multi_ci_upper <dbl>, multi_p <dbl>
set.seed(42)
dat <- data.frame(
  response = rbinom(60, 1, 0.4),
  age = rnorm(60, 60, 8),
  sex = factor(sample(c("F", "M"), 60, replace = TRUE))
)
logistic_table(dat, outcome = "response")
#> # A tibble: 2 × 13
#>   term  variable level     n n_event or_univariable univ_ci_lower univ_ci_upper
#>   <chr> <chr>    <chr> <int>   <int>          <dbl>         <dbl>         <dbl>
#> 1 age   age      NA       60      31          1.03          0.961          1.11
#> 2 sexM  sex      M        60      31          0.729         0.256          2.08
#> # ℹ 5 more variables: univ_p <dbl>, or_multivariable <dbl>,
#> #   multi_ci_lower <dbl>, multi_ci_upper <dbl>, multi_p <dbl>