README

Tables are the key format in which epidemiologists present their results. Many results tables in applied studies merely show point estimates and confidence intervals, or even p-values, from regression models: a “growing rift between epidemiologists and their data” (Rothman 2017). “Actual,” descriptive data, such as counts stratified by exposure and a main confounder or effect modifier, are often lacking.

rifttable creates presentation-ready results tables for epidemiologists in an automated, reproducible fashion. The user provides the final analytical dataset and specifies the design of the table, with rows and/or columns defined by exposure(s), effect modifier(s), and estimands as desired, allowing to show descriptors and inferential estimates in one table – bridging the rift between epidemiologists and their data, one table at a time.

Installation

install.packages("rifttable")

remotes::install_github("stopsack/rifttable")

The latter installation procedure requires the remotes package, obtainable via install.packages("remotes").

Example

library(rifttable)

example_design <- tibble::tribble(
  ~label,                       ~type,                   ~stratum,
  "Overall",                    "",                      "",
  "– Deaths/N",                 "outcomes/total",        c("Low", "High"),
  "– Risk",                     "risk",                  c("Low", "High"),
  "– Risk ratio (95% CI)",      "rr",                    c("Low", "High"),
  "– Risk difference (95% CI)", "rd",                    c("Low", "High"),
  "",                           "",                      "",
  "Low hormone receptor",       "",                      "",
  "– Deaths/N (Risk)",          "outcomes/total (risk)", "Low",
  "– Risk difference (95% CI)", "rd",                    "Low",
  "High hormone receptor",      "",                      "",
  "– Deaths/N (Risk)",          "outcomes/total (risk)", "High",
  "– Risk difference (95% CI)", "rd",                    "High"
) |> 
  dplyr::mutate(
    exposure = "stage",
    outcome = "death",
    effect_modifier = "receptor"
  )

rifttable(
  design = example_design,
  data = risks::breastcancer
)

Stage	Stage I	Stage II	Stage III
Overall
– Deaths/N	7/67	26/96	21/29
– Risk	0.10	0.27	0.72
– Risk ratio (95% CI)	1 (reference)	2.59 (1.20, 5.6)	6.9 (3.3, 14)
– Risk difference (95% CI)	0 (reference)	0.17 (0.05, 0.28)	0.62 (0.44, 0.80)

Low hormone receptor
– Deaths/N (Risk)	2/12 (0.17)	9/22 (0.41)	12/14 (0.86)
– Risk difference (95% CI)	0 (reference)	0.24 (-0.05, 0.54)	0.69 (0.41, 0.97)
High hormone receptor
– Deaths/N (Risk)	5/55 (0.09)	17/74 (0.23)	9/15 (0.60)
– Risk difference (95% CI)	0 (reference)	0.14 (0.02, 0.26)	0.51 (0.25, 0.77)

Automated, Reproducible Generation of Results Tables: Bridging the Rift Between Epidemiologists and Their Data

Installation

Example