The Reverse Fragility Index for Mortality End Points in Randomized Trials Comparing Uncemented and Cemented Hemiarthroplasty for Intracapsular Hip Fractures.

ABSTRACT

BACKGROUND: Interpreting clinical relevance of randomized clinical trials (RCTs) is challenging when P-values are marginally above or below the P = .05 threshold. This study examined the robustness of statistically insignificant mortality events from RCTs comparing hemiarthroplasty femoral fixation for displaced intracapsular hip fractures through the reverse fragility index (RFI). METHODS: RCTs were identified using Pubmed, OVID/Medline, and Cochrane databases. Mortality endpoints were stratified into 3 categories (1) within 30-days, (2) within 90-days, and (3) at latest follow-up. The RFI was derived by manipulating reported mortality events utilizing a contingency table while maintaining a constant number of participants. The reverse fragility quotient (RFQ) was quantified by dividing the RFI by the study sample. RESULTS: Eight RCTs (2,494 participants) were included. The median RFI and RFQ within 30-days was 3.0 (interquartile range [IQR] 3.0 to 6.0) and 0.016 (IQR 0.015 to 0.021), suggesting nonsignificant findings were contingent on 1.6 mortality events/100 participants. The median RFI and RFQ within 90-days was 6.0 (IQR 4.0 to 7.0) and 0.028 (IQR 0.024 to 0.038), suggesting nonsignificant findings were contingent on 2.8 mortality events/100 participants. At latest follow-up, the median RFI and RFQ was 7.0 (IQR 6.0 to 12.0) and 0.038 (IQR 0.029 to 0.054), suggesting nonsignificant findings were contingent on only 3.8 mortality events/100 participants. Median loss to follow-up was 16.0 (IQR 11.0 to 58.0; 228% greater than RFI), and exceeded the RFI in 6/7(85.7%) studies. CONCLUSIONS: A small number of events (median of 7) was required to convert a statistically nonsignificant finding to one that is significant for the endpoint of mortality. The median loss to follow-up exceeded the median RFI by greater than 200%, suggesting methodological limitations such as patient allocation could alter conclusions.