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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.
Health stats visible at Monitor.