Available online 2 May 2023

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It is important for physicians to be familiar with statistical techniques commonly used in published medical research. Statistical errors in medical literature are common, and there is a reported lack of understanding regarding statistical knowledge necessary for data interpretation and journal reading. As study design has become increasingly complex, peer-reviewed literature poorly addresses and explains the most common statistical methods utilized across leading orthopedic journals.

Methods

Articles from 5 leading general and subspecialty orthopedic journals were compiled from 3 distinct time periods. After exclusions were applied, 9521 remained, and a random 5% sampling of these articles, balanced across journals and years, was conducted yielding 437 articles after additional exclusions. Information regarding the number of statistical tests used, power/sample size calculation, type of statistical tests used, level of evidence (LOE), study type, and study design was collected.

Results

The mean number of statistical tests across all 5 orthopedic journals increased from 1.39 to 2.29 by 2018 (p = 0.007). The percentage of articles that reported power/sample size analyses was not found to differ by year, but the value has increased from 2.6% in 1994 to 21.6% in 2018 (p = 0.081). The most commonly used statistical test was the t-test which was present in 20.5% of articles, followed by chi-square test (13%), Mann−Whitney analysis (12.6%) and analysis of variance (ANOVA, 9.6%). The mean number of tests was generally greater in articles from higher impact factor journals (p = 0.013). Studies with a LOE of I used the highest mean number of statistical tests (3.23) compared to studies with lower LOE ratings (range 1.66-2.69, p < 0.001). Randomized control trials used the highest mean number of statistical test (3.31), while case series used the lowest mean number of tests (1.57, p < 0.001).

ConclusionS

The mean number of statistical tests used per article has increased over the past 25 years with the t-test, chi-square test, Mann−Whitney analysis, and ANOVA being the most used statistical tests in leading orthopedic journals. Despite an increase in statistical tests it should be noted that there was a paucity in advance statistical testing within the orthopedic literature. This study displays important trends in data analysis and can serve as a guide to help clinicians and trainees better understand the statistics used in literature as well as identifying deficits within the literature that should be addressed to help progress the field of orthopedics.

Section snippetsINTRODUCTION

Statistical analysis is a fundamental component of medical research, yet for those with no more than a basic understanding of statistics, navigating the scientific literature can be difficult. Readers often rely upon the peer-review process to ensure that the methodologic aspects of a study, including the statistical analysis, are correct. However, it has been demonstrated that the peer-reviewed literature is replete with statistical errors, bringing some study results into question.1, 2, 3, 4,

MATERIALS AND METHODS

The process of data collection involved 3 steps. The initial step was to select orthopedic journals used in the analysis. The second step was to select the articles for review. The final step involved the extraction of information regarding statistical tests and assigning level of evidence (LOE) grades to each published article.

RESULTS

Of the 437 articles reviewed, 38 were from 1994; 40 were from 2004; 74 were from 2014; 62 were from 2015; 70 were from 2016; 79 were from 2017; and 74 were from 2018. Study design quantities for clinical articles included the following: 38 randomized control trials, 47 prospective cohort studies, 142 retrospective cohort studies, 7 case-control studies, 10 cross-sectional studies, 3 meta-analysis, and 112 case series. Article counts from each of the following journals were as follows: AJSM

DISCUSSION

Statistical analysis provides the support and credibility to results that ultimately guide patient care. The American Statistical Association states that “[. . .] the use of statistics in medical research may affect whether individuals live or die, whether their health is protected or jeopardized, and whether medical science advances or gets sidetracked.”15 The importance of the proper use and understanding of statistical tests in order to sort through and effectively publish information is

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