Statistical fragility of outcomes in acellular dermal matrix literature: A systematic review of randomized controlled trials

J Plast Reconstr Aesthet Surg. 2024 Apr:91:284-292. doi: 10.1016/j.bjps.2024.02.047. Epub 2024 Feb 11.

Abstract

Background: Acellular dermal matrix (ADM) is commonly used in plastic and reconstructive surgery. With the abundance of randomized controlled trials (RCTs) reporting P-values for ADM outcomes, this study used the fragility index (FI), reverse fragility index (rFI), and fragility quotient (FQ) to evaluate the statistical stability of the outcomes in ADM RCTs.

Methods: PubMed, Embase, SCOPUS, Medline, and Cochrane databases were reviewed for ADM RCTs (2003-present) reporting a dichotomous, categorical outcome. FI and rFI (event reversals influencing outcome significance) and FQ (standardized fragility) were calculated and reported as median. Subgroup analysis was performed based on intervention types.

Results: Among the 127 studies screened, 56 RCTs with 579 outcomes were included. The median FI stood at 4 (3-5) and FQ was 0.04 (0.03-0.07). Only 101 outcomes were statistically significant with a median FI of 3 (1-6) and FQ of 0.04 (0.02-0.08). The nonsignificant outcomes had a median FI of 4 (3-5) and FQ of 0.04 (0.03-0.07). Notably, 26% of the outcomes had several patients lost to follow up equal to or surpassing the FI. Based on the intervention type, the median FIs showed minor fluctuations but remained low.

Conclusions: Outcomes from ADM-related RCTs were statistically fragile. Slight outcome reversals or maintenance of patient follow-up can alter the significance of results. Therefore, future researchers are recommended to jointly report FI, FQ, and P-values to offer a comprehensive view of the robustness in ADM literature.

Keywords: Acellular dermal matrix; Fragility index; Fragility quotient; Statistical fragility.

Publication types

  • Systematic Review

MeSH terms

  • Acellular Dermis*
  • Databases, Factual
  • Humans
  • Randomized Controlled Trials as Topic
  • Research Design