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Inflection points in longitudinal models: Tracking recovery and return to play following concussion

机译:纵向型号的拐点:跟踪恢复并返回剧烈震荡

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The return to play (RTP) process may occur during longitudinal studies tracking recovery after concussion. This factor, which is often omitted within statistical designs, could affect the fit and overall interpretation of the statistical model. This article demonstrates the difference in results and interpretation between 2 linear mixed‐model designs: (1) a between‐group longitudinal (GROUP) analysis and (2) a between‐group longitudinal model that used an inflection point to account for changes around the time of RTP (RTP analysis). These analyses were conducted on instrumented balance data collected on 23 concussed athletes and 25 controls over 8?weeks following concussion. Total sway area and the range of mediolateral acceleration were used as outcome measures. No significant findings were found in the GROUP design for either outcome measure. In contrast, the RTP analysis revealed significant effects of time ( P? =?.007) and RTP change ( P? =?.007), and group*time ( P? =?.028) and group*RTP change ( P? =?.022) interactions for total sway area, and effects of group ( P? =?.011), time ( P? =?.010), and RTP change ( P? =?.014), and group*time ( P = .013) and group*RTP change interactions ( P? =?.013) for range of mediolateral acceleration. For both outcomes, the RTP model fit the data significantly better on comparison of likelihood ratios ( P? ≤?.027). These results suggest that allowing for an inflection point in the statistical design may assist understanding of what happens around clinically meaningful time points. The choice of statistical model had a considerable effect on the interpretation of findings, and provokes discussion around the best method for analyzing longitudinal datasets when important clinical time points like RTP exist.
机译:在震荡后,可能发生返回播放(RTP)过程在震荡之后跟踪恢复。通常在统计设计内省略的该因素可能影响统计模型的拟合和整体解释。本文展示了2个线性混合模型设计的结果和解释差异:(1)组纵向(组)分析和(2)在使用拐点的组纵向模型中,以解释周围的变化RTP的时间(RTP分析)。这些分析是在23名焦点运动员收集的仪表平衡数据上进行的,并在脑震荡之后8个月的25个控制。总摇摆区域和中源性加速度的范围用作结果措施。在组设计中没有发现任何显着的发现,以进行结果措施。相比之下,RTP分析显示了时间的显着效果(p?=Δ.007)和RTP改变(p?=Δ.007)和组*时间(p?=Δ.028)和组* RTP改变(P ?=α.022)总摇摆区域的相互作用,以及组的效果(P?=Δ.011),时间(p?=Δ.010)和RTP改变(p?=Δ.014)和组*时间(p = .013)和组* RTP改变相互作用(p?=Δ.013),用于Mediolate加速度的范围。对于两种结果,RTP模型在比较似然比的比较方面会更好地拟合数据(P?≤≤027)。这些结果表明,允许统计设计中的拐点可以帮助了解临床上有意义的时间点周围发生的事情。统计模型的选择对调查结果的解释具有相当大的影响,并且激发围绕当存在RTP等重要临床时间点时分析纵向数据集的最佳方法的讨论。

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