Type:	Package
Title:	Results Tables to Bridge the Rift Between Epidemiologists and Their Data
Version:	0.7.1
Description:	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. See Rothman (2017) <doi:10.1007/s10654-017-0314-3>.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.3.2
Depends:	R (≥ 4.1.0)
Imports:	broom (≥ 0.7.0), dplyr (≥ 1.0.8), purrr, risks (≥ 0.4.3), rlang (≥ 0.4.0), stats, survival, stringr, tibble, tidyr
Suggests:	gt (≥ 0.8.0), knitr, markdown, quantreg, rmarkdown, sandwich, testthat (≥ 3.0.0)
VignetteBuilder:	knitr
URL:	https://stopsack.github.io/rifttable/, https://github.com/stopsack/rifttable/
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-06-04 18:22:41 UTC; stopsack
Author:	Konrad H. Stopsack [aut, cre, cph]
Maintainer:	Konrad H. Stopsack <stopsack@post.harvard.edu>
Repository:	CRAN
Date/Publication:	2025-06-06 13:00:02 UTC

Results Tables for Epidemiology

Description

This function displays descriptive and inferential results for binary, continuous, and survival data in the format of a table stratified by exposure and, if requested, by effect modifiers.

This function is intended only for tabulations of final results. Model diagnostics for regression models need to be conducted separately.

Usage

rifttable(
  design,
  data,
  id = "",
  layout = "rows",
  factor = 1000,
  risk_percent = FALSE,
  risk_digits = dplyr::if_else(risk_percent == TRUE, true = 0, false = 2),
  diff_digits = 2,
  ratio_digits = 2,
  ratio_digits_decrease = c(`2.995` = -1, `9.95` = -2),
  rate_digits = 1,
  to = ", ",
  reference = "(reference)",
  type2_layout = "rows",
  overall = FALSE,
  exposure_levels = c("noempty", "nona", "all")
)

Arguments

Details

The main input parameter is the dataset design. Always required are the column type (the type of requested statistic, see below), as well as outcome for binary outcomes or time and event for survival outcomes:

• label A label for each row (or column). If missing, type will be used as the label.

• exposure Optional. The exposure variable. Must be categorical (factor or logical). If missing (NA), then an unstratified table with absolute estimates only will be returned.

• outcome The outcome variable for non-survival data (i.e., whenever event and time are not used). For risk/prevalence data, this variable must be 0/1 or FALSE/TRUE.

• time The time variable for survival data. Needed for, e.g., type = "hr" and type = "rate" (i.e., whenever outcome is not used).

• time2 The second time variable for late entry models. Only used in conjunction with time. If provided, time will become the entry time and time2 the exit time, following conventions of Surv.

• event The event variable for survival data. Events are typically 1, censored observations 0. If competing events are present, censoring should be the first-ordered level, e.g., of a factor, and the level corresponding to the event of interest should be supplied as event = "event_variable@Recurrence" if "Recurrence" is the event of interest. The event variable is needed for, e.g., type = "hr" and type = "rate", i.e., whenever outcome is not used.

• trend Optional. For regression models, a continuous representation of the exposure, for which a slope per one unit increase ("trend") will be estimated. Must be a numeric variable. If joint models for exposure and effect_modifier are requested, trends are still reported within each stratum of the effect_modifier. Use NA to leave blank.

• effect_modifier Optional. A categorical effect modifier variable. Use NA to leave blank.

• stratum Optional. A stratum of the effect modifier. Use NULL to leave blank. NA will evaluate observations with missing data for the effect_modifier.

• confounders Optional. A string in the format "+ var1 + var2" that will be substituted into into formula = exposure + confounders. Use NA or "" (empty string) to leave blank; the default. For Cox models, can add "+ strata(site)" to obtain models with stratification by, e.g., site. For Poisson models, can add "+ offset(log(persontime))" to define, e.g., persontime as the offset.

• weights Optional. Variable with weights, for example inverse- probability weights. Used by comparative survival estimators (e.g., type = "hr" and type = "cumincdiff") as well as type = "cuminc" and type = "surv". They are ignored by other estimators. The spelling weight is also accepted as a fallback.

• type The statistic requested (case-insensitive):

  Comparative estimates with 95% confidence intervals:

  • "hr" Hazard ratio from Cox proportional hazards regression.

  • "irr" Incidence rate ratio for count outcomes from Poisson regression model.

  • "irrrob" Ratio for other outcomes from Poisson regression model with robust (sandwich) standard errors.

  • "rr" Risk ratio (or prevalence ratio) from riskratio. Can request specific model fitting approach and, for marginal standardization only, the number of bootstrap repeats. Examples: "rrglm_start" or "rrmargstd 2000".

  • "rd" Risk difference (or prevalence difference) from riskdiff. Can request model fitting approach and bootstrap repeats as for "rr".

  • "diff" Mean difference from linear model.

  • "quantreg" Quantile difference from quantile regression using rq with method = "fn". By default, this is the difference in medians. For a different quantile, e.g., the 75th percentile, use "quantreg 0.75".

  • "fold" Fold change from generalized linear model with log link (i.e., ratio of arithmetic means).

  • "foldlog" Fold change from linear model after log transformation of the outcome (i.e., ratio of geometric means).

  • "or" Odds ratio from logistic regression.

  • "survdiff" Difference in survival from Kaplan-Meier estimator. Provide time horizon, e.g., "survdiff 2.5" to evaluate differences in survival at 2.5 years. Uses survdiff_ci.

  • "cumincdiff" Difference in cumulative incidence from the Kaplan-Meier estimator or, if competing risks are present, its generalized form, the Aalen-Johansen estimator. Provide time horizon, e.g., "cumincdiff 2.5" to evaluate differences in cumulative incidence at 2.5 years. Uses survdiff_ci.

  • "survratio" Ratio in survival from Kaplan-Meier estimator. Provide time horizon, e.g., "survdiff 2.5" to evaluate 2.5-year relative risk. Uses survdiff_ci.

  • "cumincratio" Ratio in cumulative incidence from the Kaplan-Meier estimator or, if competing risks are present, its generalized form, the Aalen-Johansen estimator. Provide time horizon, e.g., "cumincdiff 2.5" to evaluate the 2.5-year risk difference. Uses survdiff_ci.

  Absolute estimates per exposure category:

  • "events" Event count.

  • "time" Person-time.

  • "outcomes" Outcome count.

  • "total" Number of observations.

  • "events/time" Events slash person-time.

  • "events/total" Events slash number of observations.

  • "cases/controls" Cases and non-cases (events and non-events); useful for case-control studies.

  • "risk" Risk (or prevalence), calculated as a proportion, i.e., outcomes divided by number of observations. Change between display as proportion or percent using the parameter risk_percent.

  • "risk (ci)" Risk with 95% confidence interval (Wilson score interval for binomial proportions, see scoreci).

  • "cuminc" Cumulative incidence ("risk") from the Kaplan-Meier estimator or, if competing risks are present, its generalized form, the Aalen-Johansen estimator. Provide time point (e.g., 1.5-year cumulative incidence) using "cuminc 1.5". If no time point is provided, the cumulative incidence at end of follow-up is returned. Change between display as proportion or percent using the parameter risk_percent.

  • "cuminc (ci)" Cumulative incidence ("risk"), as above, with 95% confidence intervals (Greenwood standard errors with log transformation, the default of the survival package/ survfit). Provide time point as in "cuminc".

  • "surv" Survival from the Kaplan-Meier estimator. Provide time point (e.g., 1.5-year survival) using "surv 1.5". If no time point is provided, returns survival at end of follow-up. Change between display as proportion or percent using the parameter risk_percent.

  • "surv (ci)" Survival from the Kaplan-Meier estimator with 95% confidence interval (Greenwood standard errors with log transformation, the default of the survival package/survfit). Provide time point as in "surv".

  • "rate" Event rate: event count divided by person-time, multiplied by factor.

  • "rate (ci)" Event rate with 95% confidence interval (Poisson-type large-sample interval).

  • "outcomes (risk)" A combination: Outcomes followed by risk in parentheses.

  • "outcomes/total (risk)" A combination: Outcomes slash total followed by risk in parentheses.

  • "events/time (rate)" A combination: Events slash time followed by rate in parentheses.

  • "medsurv" Median survival.

  • "medsurv (ci)" Median survival with 95% confidence interval.

  • "medfu" Median follow-up (reverse Kaplan-Meier), equals median survival for censoring.

  • "medfu (iqr)" Median and interquartile range for follow-up.

  • "maxfu" Maximum follow-up time.

  • "mean" Mean (arithmetic mean).

  • "mean (ci)" Mean and 95% CI.

  • "mean (sd)" Mean and standard deviation.

  • "geomean" Geometric mean.

  • "median" Median.

  • "median (iqr)" Median and interquartile range.

  • "range" Range: Minimum to maximum value.

  • "sum" Sum.

  • "blank" or "" An empty line.

  • Custom: A custom function that must be available under the name estimate_my_function in order to be callable as type = "my_function".

  By default, regression models will be fit separately for each stratum of the effect_modifier. Append "_joint" to "hr", "rr", "rd", "irr", "irrrob", "diff", "fold", "foldlog", "quantreg", or "or" to obtain "joint" models for exposure and effect modifier that have a single reference category. Example: type = "hr_joint". The reference categories for exposure and effect modifier are their first factor levels, which can be changed using fct_relevel from the forcats package. Note that the joint model will be fit across all non-missing (NA) strata of the effect modifier, even if the design table does not request all strata be shown.

• type2 Optional. A second statistic that is added in an adjacent row or column (global option type2_layout defaults to "row" and can alternatively be set to "column"). For example, use type = "events/times", type2 = "hr" to get both event counts/person-time and hazard ratios for the same data, exposure, stratum, confounders, and outcome.

• digits Optional. The number of digits for rounding an individual line. Defaults to NA, where the number of digits will be determined based on rifttable's arguments risk_percent, risk_digits, diff_digits, ratio_digits, or rate_digits, as applicable.

• digits2 Optional. As digits, for the second estimate (type2).

• nmin. Optional. Suppress estimates with "--" if a cell defined by exposure, and possibly the effect modifier, contains fewer observations or, for survival analyses, fewer events than nmin. Defaults to NA, i.e., to print all estimates.

• na_rm. Optional. Exclude observations with missing outcome. Defaults to FALSE. Use with caution.

• ci. Optional. Confidence level. Defaults to 0.95.

Use tibble, tribble, and mutate to construct the design dataset, especially variables that are used repeatedly (e.g., exposure, time, event, or outcome). See examples.

If regression models cannot provide estimates in a stratum, e.g., because there are no events, then "--" will be printed. Accompanying warnings need to be suppressed manually, if appropriate, using suppressWarnings(rifttable(...)).

Value

Tibble. Get formatted output as a gt table by passing on to rt_gt.

References

Greenland S, Rothman KJ (2008). Introduction to Categorical Statistics. In: Rothman KJ, Greenland S, Lash TL. Modern Epidemiology, 3rd edition. Philadelpha, PA: Lippincott Williams & Wilkins. Page 242. (Poisson/large-sample approximation for variance of incidence rates)

Examples

# Load 'cancer' dataset from survival package (Used in all examples)
data(cancer, package = "survival")

# The exposure (here, 'sex') must be categorical
cancer <- cancer |>
  tibble::as_tibble() |>
  dplyr::mutate(
    sex = factor(
      sex,
      levels = 1:2,
      labels = c("Male", "Female")
    ),
    time = time / 365.25,
    status = status - 1
  )


# Example 1: Binary outcomes (use 'outcome' variable)
# Set table design
design1 <- tibble::tibble(
  label = c(
    "Outcomes",
    "Total",
    "Outcomes/Total",
    "Risk",
    "Risk (CI)",
    "Outcomes (Risk)",
    "Outcomes/Total (Risk)",
    "RR",
    "RD"
  )
) |>
  dplyr::mutate(
    type = label,
    exposure = "sex",
    outcome = "status"
  )

# Generate rifttable
rifttable(
  design = design1,
  data = cancer
)

# Use 'design' as columns (selecting RR and RD only)
rifttable(
  design = design1 |>
    dplyr::filter(label %in% c("RR", "RD")),
  data = cancer,
  layout = "cols"
)


# Example 2: Survival outcomes (use 'time' and 'event'),
#   with an effect modifier and a confounder
# Set table design
design2 <- tibble::tribble(
  # Elements that vary by row:
  ~label,                       ~stratum, ~confounders, ~type,
  "**Overall**",                NULL,     "",           "blank",
  "  Events",                   NULL,     "",           "events",
  "  Person-years",             NULL,     "",           "time",
  "  Rate/1000 py (95% CI)",    NULL,     "",           "rate (ci)",
  "  Unadjusted HR (95% CI)",   NULL,     "",           "hr",
  "  Age-adjusted HR (95% CI)", NULL,     "+ age",      "hr",
  "",                           NULL,     "",           "blank",
  "**Stratified models**",      NULL,     "",           "",
  "*ECOG PS1* (events/N)",      1,        "",           "events/total",
  "  Unadjusted",               1,        "",           "hr",
  "  Age-adjusted",             1,        "+ age",      "hr",
  "*ECOG PS2* (events/N)",      2,        "",           "events/total",
  "  Unadjusted",               2,        "",           "hr",
  "  Age-adjusted",             2,        "+ age",      "hr",
  "",                           NULL,     "",           "",
  "**Joint model**, age-adj.",  NULL,     "",           "",
  "  ECOG PS1",                 1,        "+ age",      "hr_joint",
  "  ECOG PS2",                 2,        "+ age",      "hr_joint"
) |>
  # Elements that are the same for all rows:
  dplyr::mutate(
    exposure = "sex",
    event = "status",
    time = "time",
    effect_modifier = "ph.ecog"
  )

# Generate rifttable
rifttable(
  design = design2,
  data = cancer |>
    dplyr::filter(ph.ecog %in% 1:2)
)


# Example 3: Get two estimates using 'type' and 'type2'
design3 <- tibble::tribble(
  ~label,     ~stratum, ~type,          ~type2,
  "ECOG PS1", 1,        "events/total", "hr",
  "ECOG PS2", 2,        "events/total", "hr"
) |>
  dplyr::mutate(
    exposure = "sex",
    event = "status",
    time = "time",
    confounders = "+ age",
    effect_modifier = "ph.ecog"
  )

rifttable(
  design = design3,
  data = cancer |>
    dplyr::filter(ph.ecog %in% 1:2)
)

rifttable(
  design = design3,
  data = cancer |>
    dplyr::filter(ph.ecog %in% 1:2),
  layout = "cols",
  type2_layout = "cols"
)


# Example 4: Continuous outcomes (use 'outcome' variable);
# request rounding to 1 decimal digit in some cases;
# add continuous trend (slope per one unit of the 'trend' variable)
tibble::tribble(
  ~label,                   ~stratum, ~type,        ~digits,
  "Marginal mean (95% CI)", NULL,     "mean (ci)",  1,
  "  Male",                 "Male",   "mean",       NA,
  "  Female",               "Female", "mean",       NA,
  "",                       NULL,     "",           NA,
  "Stratified model",       NULL,     "",           NA,
  "  Male",                 "Male",   "diff",       1,
  "  Female",               "Female", "diff",       1,
  "",                       NULL,     "",           NA,
  "Joint model",            NULL,     "",           NA,
  "  Male",                 "Male",   "diff_joint", NA,
  "  Female",               "Female", "diff_joint", NA
) |>
  dplyr::mutate(
    exposure = "ph.ecog_factor",
    trend = "ph.ecog",
    outcome = "age",
    effect_modifier = "sex"
  ) |>
  rifttable(
    data = cancer |>
      dplyr::filter(ph.ecog < 3) |>
      dplyr::mutate(ph.ecog_factor = factor(ph.ecog))
  )


# Example 5: Get formatted output for Example 2
rifttable(
  design = design2,
  data = cancer |>
    dplyr::filter(ph.ecog %in% 1:2)
) |>
  rt_gt()

Turn tibble into gt Table with Custom Formatting

Description

Formatting includes:

• Text align to top/left

• Smaller row padding

• No top border

• Bold column labels

If this function is called within a document that is being knit to plain markdown, such as format: gfm in a Quarto document or format: github_document in an RMarkdown document, then a plain markdown-formatted table (e.g., without footnotes) is returned via kable.

Usage

rt_gt(df, md = 1, indent = 10, remove_border = TRUE)

Arguments

Value

Formatted gt table

Examples

data(mtcars)
mtcars |>
  dplyr::slice(1:5) |>
  rt_gt()

Wilson Score Confidence Intervals

Description

"This function computes a confidence interval for a proportion. It is based on inverting the large-sample normal score test for the proportion." (Alan Agresti, who wrote the original R code)

Inputs for success, total, and level are vectorized.

Usage

scoreci(success, total, level = 0.95, return_midpoint = FALSE)

Arguments

Value

Data frame:

• success Success count

• total Total count

• estimate Proportion

• conf.low Lower bound of the confidence interval.

• conf.high Upper bound of the confidence interval.

• midpoint Mid-point of the confidence interval (for return_midpoint = TRUE).

• level Confidence level.

See Also

https://users.stat.ufl.edu/~aa/cda/R/one-sample/R1/index.html

Agresti A, Coull BA. Approximate is better than "exact" for interval estimation of binomial proportions. Am Stat 1998;52:119-126. doi:10.2307/2685469

Brown LD, Cai TT, DasGupta A. Interval estimation for a binomial proportion (with discussion). Stat Sci 2001;16:101-133. doi:10.1214/ss/1009213286

Examples

scoreci(success = 5, total = 10)
scoreci(success = c(5:10), total = 10, level = 0.9)

Estimate Difference in Survival or Cumulative Incidence and Confidence Interval

Description

This function estimates the unadjusted difference or ratio in survival or cumulative incidence (risk) at a given time point based on the difference between per-group Kaplan-Meier estimates or, if competing events are present, Aalen-Johansen estimates of the cumulative incidence.

For constructing confidence limits, the MOVER approach described by Zou and Donner (2008) is used, with estimation on the log scale for ratios.

Usage

survdiff_ci(
  formula,
  data,
  time,
  estimand = c("survival", "cuminc"),
  type = c("diff", "ratio"),
  approach = c("mover", "squareadd"),
  conf.level = 0.95,
  event_type = NULL,
  id_variable = NULL,
  weighted = FALSE
)

Arguments

Value

Tibble in tidy format:

• term Name of the exposure stratum.

• estimate Difference or ratio.

• std.error Large-sample standard error of the difference in survival functions (see References). For each survival function, Greenwood standard errors with log transformation are used, the default of the survival package/survfit).

• statistic z statistic.

• p.value From the z statistic.

• conf.low Lower confidence limit

• conf.high Upper confidence limit

References

Com-Nougue C, Rodary C, Patte C. How to establish equivalence when data are censored: a randomized trial of treatments for B non-Hodgkin lymphoma. Stat Med 1993;12:1353–64. doi:10.1002/sim.4780121407

Altman DG, Andersen PK. Calculating the number needed to treat for trials where the outcome is time to an event. BMJ 1999;319:1492–5. doi:10.1136/bmj.319.7223.1492

Zou GY, Donner A. Construction of confidence limits about effect measures: A general approach. Statist Med 2008;27:1693–1702. doi:10.1002/sim.3095

Examples

# Load 'cancer' dataset from survival package (Used in all examples)
data(cancer, package = "survival")

cancer <- cancer |>
  dplyr::mutate(
    sex = factor(
      sex,
      levels = 1:2,
      labels = c("Male", "Female")
    ),
    status = status - 1
  )

survdiff_ci(
  formula = survival::Surv(time = time, event = status) ~ sex,
  data = cancer,
  time = 365.25
)
# Females have 19 percentage points higher one-year survival than males
# (95% CI, 5 to 34 percentage points).

Design A Descriptive Table

Description

This function generates a design table from which rifttable can generate a descriptive table.

Usage

table1_design(
  data,
  ...,
  by = NULL,
  total = TRUE,
  empty_levels = FALSE,
  na_always = FALSE,
  na_label = "Unknown",
  continuous_type = "median (iqr)",
  binary_type = "outcomes (risk)"
)

Arguments

Value

design tibble that can be passed on to rifttable. Contains an attribute rt_data so that the dataset does not have to be provided to rifttable another time.

Examples

# Data preparation
cars <- tibble::as_tibble(mtcars) |>
  dplyr::mutate(
    gear = factor(
      gear,
      levels = 3:5,
      labels = c("Three", "Four", "Five")
    ),
    # Categorical version of "hp", shows each category
    hp_categorical = dplyr::if_else(
      hp >= 200,
      true = "200+ hp",
      false = "<200 hp"
    ),
    # Binary version of "hp", shows the TRUEs
    hp_binary = hp >= 200
  )
# Label some variables. Better alternative: labelled::set_variable_labels()
attr(cars$hp, "label") <- "Horsepower"
attr(cars$hp_categorical, "label") <- "Horsepower"
attr(cars$hp_binary, "label") <- "200+ hp"
attr(cars$am, "label") <- "Automatic transmission"
attr(cars$gear, "label") <- "Gears"

# Generate table "design"
design <- cars |>
  table1_design(
    hp, hp_categorical, hp_binary, mpg, am,
    by = gear
  )

# Use "design" to create a descriptive table.
design |>
  rifttable(diff_digits = 0)

# Obtain a formatted table
design |>
  rifttable(diff_digits = 0) |>
  rt_gt()