@article{fdi:010090166,
  title = {{P}erformance of four centralized statistical monitoring methods for early detection of an atypical center in a multicenter study},
  author = {{N}iangoran, {S}. and {J}ournot, {V}. and {M}arcy, {O}livier and {A}nglaret, {X}. and {A}lioum, {A}.},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground: {E}nsuring the quality of data is essential for the credibility of a multicenter clinical trial. {C}entralized {S}tatistical {M}onitoring ({CSM}) of data allows the detection of a center in which the distribution of a specific variable is atypical compared to other centers. {T}he ideal {CSM} method should allow early detection of problem and therefore involve the fewest possible participants. {M}ethods: {W}e simulated clinical trials and compared the performance of four {CSM} methods ({S}tudent, {H}atayama, {D}esmet, {D}istance) to detect whether the distribution of a quantitative variable was atypical in one center in relation to the others, with different numbers of participants and different mean deviation amplitudes.{R}esults: {T}he {S}tudent and {H}atayama methods had good sensitivity but poor specificity, which disqualifies them for practical use in {CSM}. {T}he {D}esmet and {D}istance methods had very high specificity for detecting all the mean deviations tested (including small values) but low sensitivity with mean deviations less than 50%.{C}onclusion: {A}lthough the {S}tudent and {H}atayama methods are more sensitive, their low specificity would lead to too many alerts being triggered, which would result in additional unnecessary control work to ensure data quality. {T}he {D}esmet and {D}istance methods have low sensitivity when the deviation from the mean is low, suggesting that the {CSM} should be used alongside other conventional monitoring procedures rather than replacing them. {H}owever, they have excellent specificity, which suggests they can be applied routinely, since using them takes up no time at central level and does not cause any unnecessary workload in investigating centers.},
  keywords = {{D}ata quality ; {C}entralized {S}tatistical monitoring ; {M}ulticenter clinical trial ; {S}ensitivity ; {S}pecificity},
  booktitle = {},
  journal = {{C}ontemporary {C}linical {T}rials {C}ommunications},
  volume = {34},
  numero = {},
  pages = {101168 [8 p.]},
  year = {2023},
  DOI = {10.1016/j.conctc.2023.101168},
  URL = {https://www.documentation.ird.fr/hor/fdi:010090166},
}