Reasons for the Reporting Behavior of Japanese Collegiate Rugby Union Players Regarding Suspected Concussion Symptoms: A Propensity Analysis

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Abstract

While previous research has identified the reasons for the concussion-reporting behavior of rugby union players, the influence of confounding factors such as concussion experience, education, and knowledge of concussion symptoms, any of which may have influenced the results, has not been considered. This study aimed to clarify the reasons for the reporting behavior of college rugby union players regarding suspected concussion symptoms by adjusting for confounding factors using the propensity score. A questionnaire about both concussion knowledge and concussion-reporting behavior was administered to 240 collegiate rugby union players. Of the 208 (86.7%) valid respondents to the questionnaire, 196 (94.2%) had experienced any one symptom of a suspected concussion, such as headache, at least once, and 137 (65.9%) reported symptoms to someone else. This study’s results revealed two important reasons for reporting symptoms: (1) the willingness of players to report experienced symptoms to someone else, along with realizing a concussion, and (2) the willingness of players to report suspected concussion symptoms, despite the absence of a doctor or trainer. These results suggest that providing educational opportunities to recognize suspected concussion symptoms and establishing a team culture of reporting physical problems to someone else is important for improving concussion-reporting behavior.

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A power primer.

Jacob Cohen (1992)

One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.

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An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

Peter C Austin (2011)

The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.