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FragiliTidy

Tidyverse-compatible, high-performance fragility metrics for two-arm clinical trials — for both dichotomous outcomes (Fragility Index / Reverse Fragility Index) and continuous outcomes (Continuous Fragility Index and Reverse Continuous Fragility Index).

FragiliTidy is designed to be fast (~25x faster than stats::fisher.test() / stats::chisq.test()) incorporating rejection sampling and an iterative Welch t-test substitution algorithm for the continuous indices. Everything plugs directly into tidyverse syntax.

Installation

# install.packages("remotes")
remotes::install_github("tomdrake/fragilitidy")

Functions

Function	Purpose
`fragility_index()`	Add a fragility-index column to a data frame (dichotomous outcomes).
`revfragility_index()`	Add a reverse-fragility-index column to a data frame.
`fragility_index_vec()` / `revfragility_index_vec()`	Vectorised forms for `dplyr::mutate()`.
`continuous_fragility_index()`	Add a Continuous Fragility Index column to a data frame.
`reverse_continuous_fragility_index()`	Add a Reverse Continuous Fragility Index column to a data frame.
`continuous_fragility_index_summary()`	CFI from a single set of summary statistics (mean, SD, n per arm).
`reverse_continuous_fragility_index_summary()`	Reverse CFI from a single set of summary statistics.
`continuous_fragility_index_raw()`	CFI from raw per-patient outcome vectors.
`continuous_fragility_index_vec()` / `reverse_continuous_fragility_index_vec()`	Vectorised summary-stat forms.

Quick start

Dichotomous outcomes

library(dplyr)
library(FragiliTidy)

trials <- tibble::tribble(
  ~study,    ~ie, ~ce, ~in_, ~cn,
  "Trial A",  10,  20,  100,  100,
  "Trial B",   5,  15,   80,   80,
  "Trial C",  30,  30,  200,  200
)

trials |>
  fragility_index(ie, ce, in_, cn) |>
  revfragility_index(ie, ce, in_, cn)

Continuous outcomes

trials_continuous <- tibble::tribble(
  ~study,    ~m1, ~s1, ~k1, ~m2, ~s2, ~k2,
  "Trial X",  70,  10,  50,  50,  10,  50,
  "Trial Y",  60,  15,  40,  55,  15,  40
)

trials_continuous |>
  continuous_fragility_index(m1, s1, k1, m2, s2, k2) |>
  reverse_continuous_fragility_index(m1, s1, k1, m2, s2, k2)

Or, for a single trial from summary statistics:

continuous_fragility_index_summary(
  mean1 = 70, sd1 = 10, n1 = 100,
  mean2 = 50, sd2 = 10, n2 = 100,
  seed  = 1
)

reverse_continuous_fragility_index_summary(
  mean1 = 55, sd1 = 10, n1 = 30,
  mean2 = 50, sd2 = 10, n2 = 30,
  seed  = 1
)

Background

The Fragility Index (Walsh et al., 2014) is the minimum number of event reassignments in the smaller-event arm required to flip a statistically significant dichotomous result to non-significance.
The Reverse Fragility Index is the analogous quantity for non-significant dichotomous results.
The Continuous Fragility Index (Caldwell et al., 2021) extends the concept to continuous outcomes compared via Welch’s t-test, via an iterative substitution algorithm.
The Reverse Continuous Fragility Index here estimates how many additional participants per arm would have been required to drive a non-significant continuous outcome to significance, given the observed mean and SD per arm.

See vignette("FragiliTidy") for a walkthrough.

References

Walsh M, Srinathan SK, McAuley DF, et al. The statistical significance of randomized controlled trial results is frequently fragile. J Clin Epidemiol 2014;67:622-628.
Caldwell JE, Youssefzadeh K, Limpisvasti O. A method for calculating the fragility index of continuous outcomes. J Clin Epidemiol 2021;136:20-25.

License

GPL-3. See LICENSE.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.