Sample size calculations for randomized clinical trials published in anesthesiology journals: a comparison of 2010 versus 2016

  • Jeffrey T. Y. Chow
  • Timothy P. Turkstra
  • Edmund Yim
  • Philip M. Jones
Reports of Original Investigations



Although every randomized clinical trial (RCT) needs participants, determining the ideal number of participants that balances limited resources and the ability to detect a real effect is difficult. Focussing on two-arm, parallel group, superiority RCTs published in six general anesthesiology journals, the objective of this study was to compare the quality of sample size calculations for RCTs published in 2010 vs 2016.


Each RCT’s full text was searched for the presence of a sample size calculation, and the assumptions made by the investigators were compared with the actual values observed in the results. Analyses were only performed for sample size calculations that were amenable to replication, defined as using a clearly identified outcome that was continuous or binary in a standard sample size calculation procedure.


The percentage of RCTs reporting all sample size calculation assumptions increased from 51% in 2010 to 84% in 2016. The difference between the values observed in the study and the expected values used for the sample size calculation for most RCTs was usually > 10% of the expected value, with negligible improvement from 2010 to 2016.


While the reporting of sample size calculations improved from 2010 to 2016, the expected values in these sample size calculations often assumed effect sizes larger than those actually observed in the study. Since overly optimistic assumptions may systematically lead to underpowered RCTs, improvements in how to calculate and report sample sizes in anesthesiology research are needed.

Calculs de la taille des échantillons pour les essais cliniques randomisés publiés dans les journaux d’anesthésiologie : comparaison entre 2010 et 2016



Même si chaque essai clinique randomisé (ECR) nécessite des participants, la détermination de leur nombre idéal prenant en compte d’une part des ressources limitées et d’autre part la capacité à détecter un effet réel s’avère difficile. Se concentrant sur les ECR à deux groupes, à groupes parallèles, et essais de supériorité publiés dans six journaux d’anesthésiologie, l’objectif de cette étude était de comparer la qualité des calculs de taille d’échantillon pour les ECR publiés en 2010 et en 2016.


Le texte complet de chaque ECR a été analysé en fonction du calcul de la taille de l’échantillon et les hypothèses faites par les investigateurs ont été comparées aux valeurs réelles observées dans les résultats. Des analyses n’ont été pratiquées que pour les calculs de taille d’échantillon qu’il était possible de répliquer, en utilisant une mesure clairement identifiée, continue ou binaire, avec une procédure usuelle de calcul de taille d’échantillon.


Le pourcentage des ECR indiquant toutes les hypothèses du calcul de taille de l’échantillon est passé de 51 % en 2010 à 84 % en 2016. La différence entre les valeurs observées dans les études et les valeurs attendues utilisées pour les calculs de taille d’échantillon de la majorité des ECR était habituellement plus de 10 % plus élevée que la valeur attendue, sans véritable amélioration entre 2010 et 2016.


Alors que la présentation des calculs de la taille des échantillons s’est améliorée entre 2010 et 2016, les valeurs attendues dans ces calculs ont souvent supposé des ampleurs d’effet supérieures à celles véritablement observées dans les études. Considérant que des hypothèses excessivement optimistes entraînent un manque de puissance des ECR, des améliorations sur la façon de calculer et présenter la taille des échantillons pour la recherche en anesthésiologie sont nécessaires.



We appreciate the support and advice offered by Neil Klar (PhD, Department of Epidemiology and Biostatistics, The University of Western Ontario) and Janet Martin (PharmD, Department of Anesthesia & Perioperative Medicine and Department of Epidemiology and Biostatistics, The University of Western Ontario).

Conflicts of interest

Philip M. Jones is an Associate Editor at the Canadian Journal of Anesthesia. No other authors have any conflicts of interest.

Editorial responsibility

This submission was handled by Dr. Gregory L. Bryson, Deputy Editor-in-Chief, Canadian Journal of Anesthesia.

Author contributions

Jeffrey T. Y. Chow and Philip M. Jones designed the study, analyzed the data, and wrote the manuscript. Jeffrey T. Y. Chow, Timothy P. Turkstra, Edmund Yim, and Philip M. Jones conducted the study, collected data, and revised the manuscript. Philip M. Jones supervised the study.


Jeffrey T. Y. Chow is supported by an Ontario Graduate Scholarship: Queen Elizabeth II Graduate Scholarship in Science and Technology (OGS: QEIIGSST) and the funder had no role in any part of the study. Philip M. Jones is supported by internal departmental funding. No other authors received funding for this study.


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Copyright information

© Canadian Anesthesiologists' Society 2018

Authors and Affiliations

  1. 1.Department of Epidemiology & BiostatisticsThe University of Western OntarioLondonCanada
  2. 2.Department of Anesthesia & Perioperative MedicineThe University of Western OntarioLondonCanada
  3. 3.Schulich School of Medicine & DentistryThe University of Western OntarioLondonCanada
  4. 4.Rm C3-110 - University HospitalLondon Health Sciences CentreLondonCanada

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