Tu1810 – Fragility Index of Randomized Controlled Trials in American College of Gastroenterology Guidelines on the Management of Crohn's Disease

Majeed, Muhammad; Agrawal, Rohit; Attar, Bashar M.; Omar, Yazan Abu; Gandhi, Seema; Baig, Muhammad A.

doi:10.1016/s0016-5085(19)39796-3

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, in the 2 months after this article was submitted, three additional review studies were published applying the FI. [18][19][20] Within this context, this article uniquely serves as a comprehensive statistical critique, which examines the FI in depth and illustrates its shortcomings in a way that is accessible to a variety of researchers. This article leads the reader through a statistical reasoning process considering how exactly the FI should be interpreted.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, in spite of the critical ideas in the brief critiques and letters mentioned above, researchers continue to enthusiastically apply the FI in review papers. In fact, in the 2 months after this article was submitted, three additional review studies were published applying the FI 18‐20 . Within this context, this article uniquely serves as a comprehensive statistical critique, which examines the FI in depth and illustrates its shortcomings in a way that is accessible to a variety of researchers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dismantling the Fragility Index: A demonstration of statistical reasoning

Potter

2020

Statistics in Medicine

View full text Add to dashboard Cite

The Fragility Index has been introduced as a complement to the P-value to summarize the statistical strength of evidence for a trial's result. The Fragility Index (FI) is defined in trials with two equal treatment group sizes, with a dichotomous or time-to-event outcome, and is calculated as the minimum number of conversions from nonevent to event in the treatment group needed to shift the P-value from Fisher's exact test over the .05 threshold. As the index lacks a well-defined probability motivation, its interpretation is challenging for consumers. We clarify what the FI may be capturing by separately considering two scenarios: (a) what the FI is capturing mathematically when the probability model is correct and (b) how well the FI captures violations of probability model assumptions. By calculating the posterior probability of a treatment effect, we show that when the probability model is correct, the FI inappropriately penalizes small trials for using fewer events than larger trials to achieve the same significance level. The analysis shows that for experiments conducted without bias, the FI promotes an incorrect intuition of probability, which has not been noted elsewhere and must be dispelled. We illustrate shortcomings of the FI's ability to quantify departures from model assumptions and contextualize the FI concept within current debate around the null hypothesis significance testing paradigm. Altogether, the FI creates more confusion than it resolves and does not promote statistical thinking. We recommend against its use. Instead, sensitivity analyses are recommended to quantify and communicate robustness of trial results.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%