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Checklist for Statistical Topics in Anesthesia & Analgesia Reviews

Dexter, Franklin MD, PhD

doi: 10.1213/ANE.0b013e3182204e95
Editorials: Editorials

From the Department of Anesthesia, University of Iowa, Iowa City, Iowa.

Supported by departmental funds.

Address correspondence and reprint requests to Franklin Dexter, MD, PhD, Department of Anesthesia, University of Iowa, 6JCP, 200 Hawkins Dr., Iowa City, IA 52242. Address e-mail to or

Accepted April 18, 2011

In my role as Statistics Editor for Anesthesia & Analgesia, I have reviewed >200 papers each year for the past 3 years. This editorial summarizes the recurring statistical issues identified in papers submitted to the journal. Table 1 is an annotated version of my checklist.17 The checklist relies on some basic statistical definitions. Table 2 is an annotated glossary for words and phrases in Table 1.8 Many of these definitions are taken verbatim from Wikipedia. They are shown in quotes.

Table 1

Table 1

Table 2

Table 2

Table 1 is not intended as a complete checklist. Anesthesia & Analgesia encourages authors to follow the CONSORT, STROBE, or PRISMA checklists, which cover basic topics. Table 1 includes topics not directly addressed in these checklists.

More than three-quarters of articles in general medical journals include relatively advanced topics such as those in Table 1.9,10Anesthesia & Analgesia has more mathematics and statistics than many clinical journals because of the nature of our research and practice. Performing the work in teams including analysts, statisticians, engineers, etc., helps assure that quantitative topics including those listed in Table 1 are addressed adequately.

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Name: Franklin Dexter, MD, PhD.

Role: This author helped write the manuscript.

Conflicts: Franklin Dexter reported no conflicts of interest.

Attestation: Franklin Dexter approved the final manuscript.

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1. Mascha EJ. Equivalence and noninferiority testing in anesthesiology research. Anesthesiology 2010;113:779–81
2. Austin PC, Grootendorst P, Norman SL, Anderson GM. Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study. Stat Med 2007;26:754–68
3. Strum DP, Sampson AR, May JH, Vargas LG. Surgeon and type of anesthesia predict variability in surgical procedure times. Anesthesiology 2000;92:1454–67
4. Dexter F, Epstein RH, Lee JD, Ledolter J. Automatic updating of times remaining in surgical cases using Bayesian analysis of historical case duration data and instant messaging updates from anesthesia providers. Anesth Analg 2009;108:929–40
5. Smallman B, Dexter F. Optimizing the arrival, waiting, and NPO times of children on the day of pediatric endoscopy procedures. Anesth Analg 2010;110:879–87
6. Baker WL, White CM, Cappelleri JC, Kluger J, Coleman CI; Health Outcomes, Policy, and Economics (HOPE) Collaborative Group. Understanding heterogeneity in meta-analysis: the role of meta-regression. Int J Clin Pract 2009;63:1426–34
7. Cohen MM, O'Brien-Pallas LL, Copplestone C, Wall R, Porter J. Nursing workload associated with adverse events in the postanesthesia care unit. Anesthesiology 1999;91:1882–90
8. Silber JH, Rosenbaum PR, Zhang X, Even-Shoshan O. Influence of patient and hospital characteristics on anesthesia time in medicare patients undergoing general and orthopedic surgery. Anesthesiology 2007;106:356–64
9. Strasak AM, Zaman Q, Marinell G, Pfeiffer KP, Ulmer H. The use of statistics in medical research: a comparison of The New England Journal of Medicine and Nature Medicine. Am Stat 2007;61:45–55
10. Windish DM, Huot SJ, Green ML. Medicine residents' understanding of the biostatistics and results in the medical literature. JAMA 2007;298:1010–22
© 2011 International Anesthesia Research Society