Statistical Analysis of Dependent Observations in the Orthopaedic Sports Literature.

BACKGROUND

Orthopaedic research may involve multiple observations from the same patient because of bilateral joint involvement, multiple disease sites, or recurrent disease episodes. These situations violate statistical independence and need to be accounted for via appropriate statistical techniques. Failing to account for nonindependence may lead to biased and overly precise effect estimates.

PURPOSE

To determine the degree to which orthopaedic sports medicine studies analyze dependent observations and the proportion of these failing to account for nonindependence.

STUDY DESIGN

Cross-sectional study.

METHODS

Clinical studies published in from 2012 to 2017 were reviewed. Studies reporting nonindependent observations because of multiple extremity involvement or multiple disease episodes were identified. Methods to account for nonindependence were recorded. Studies violating the assumption of independence were identified and stratified by study design, level of evidence, body part involved, and inclusion of a statistician coauthor. Univariate logistic regression was used to determine whether these factors were associated with violations of statistical independence.

RESULTS

After screening 1016 articles, 886 clinical studies were reviewed. A total of 135 (15%) studies analyzed dependent observations, and 111 (82%) of these failed to account for nonindependence. Relative to the knee, studies of the hip (odds ratio [OR], 0.21; = .02) and the thigh or leg (OR, 0.03; = .004) were less likely to violate statistical independence. Study design ( = .03) was also associated with violations of statistical independence. Among studies that analyzed dependent observations, the median proportion of dependent observations relative to the total number of observations in each study was 0.07 (interquartile range, 0.04-0.12).

CONCLUSION

The analysis of dependent observations is common in the orthopaedic sports literature, but most studies do not adjust for nonindependence in these situations. Investigators should be aware of incorrect inferences arising from nonindependence and how to statistically adjust for dependent data.