Evidence for non-random sampling in randomised, controlled trials by Yuhji Saitoh

Anaesthesia. 2017 Jan;72(1):17-27. doi: 10.1111/anae.13650.


A large number of randomised trials authored by Yoshitaka Fujii have been retracted, in part as a consequence of a previous analysis finding a very low probability of random sampling. Dr Yuhji Saitoh co-authored 34 of those trials and he was corresponding author for eight of them. We found a number of additional randomised, controlled trials that included baseline data, with Saitoh as corresponding author, that Fujii did not co-author. We used Monte Carlo simulations to analyse the baseline data from 32 relevant trials in total as well as an outcome (muscle twitch recovery ratios) reported in several. We also compared a series of muscle twitch recovery graphs appearing in a number of Saitoh's publications. The baseline data in 14/32 randomised, controlled trials had p < 0.01, of which seven p values were < 0.001. Eight trials reported four ratios of the time for the return of muscle activity after neuromuscular blockade, the distributions of which were homogeneous: the p values for the observed Q statistics were 0.0055, 0.031, 0.016 and 0.0071. Comparison of graphs revealed multiple coincident or near-coincident curves across a large number of publications, a finding also inconsistent with random sampling. Combining the continuous and categorical probabilities of the 32 included trials, we found a very low likelihood of random sampling: p = 1.27 × 10-8 (1 in 100,000,000). The high probability of non-random sampling and the repetition of lines in multiple graphs suggest that further scrutiny of Saitoh's work is warranted.

Keywords: controlled trials; data fabrication; fraud; randomised.

MeSH terms

  • Adult
  • Data Interpretation, Statistical
  • Female
  • Humans
  • Male
  • Middle Aged
  • Monte Carlo Method
  • Neuromuscular Blockade
  • Random Allocation
  • Randomized Controlled Trials as Topic / methods
  • Randomized Controlled Trials as Topic / standards*
  • Research Design / standards*
  • Scientific Misconduct / statistics & numerical data