On the validity of statistical parametric mapping for nonuniformly and heterogeneously smooth one-dimensional biomechanical data
On the validity of statistical parametric mapping for nonuniformly and heterogeneously smooth one-dimensional biomechanical data
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DOI：
10.1016/j.jbiomech.2019.05.018
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发表时间：
2019-06-25
影响因子：
2.4
通讯作者：
Liebl, Dominik
中科院分区：
文献类型：
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作者：
Pataky, Todd C.;Vanrenterghem, Jos;Liebl, Dominik
非均匀（非恒定）时间平滑度可能出现在生物力学过程（如撞击）中，异质平滑度（观察中的平滑度不相等）可能出现在力学上不同的比较中，例如填充与未填充的撞击，其中填充的动态通常比未填充的动态更平滑。据报道，统计参数映射 (SPM) 的概率值对于这种情况可能无效。本文的目的是阐明非均匀和异质平滑一维 (1D) 数据的 SPM 分析的有效性范围。我们在一系列平滑度值上模拟了各种非均匀和非均匀平滑的高斯一维数据，并计算了每种平滑度类型在 10,000 次模拟迭代中的 I 类错误率。结果表明，在所有情况下，SPM 都能准确地将误差控制在规定的 alpha = 0.05 处。此外，假阳性的分布随时间变化是均匀的，这意味着无论局部粗糙度如何，所有区域产生假阳性的可能性都是相同的。然而，我们表明，簇级推论（即特定于局部显着区域的 p 值）虽然从未超过 alpha（根据定义），但对于当前模拟的场景可能会高估或低估约 0.01。我们得出的结论是，SPM 的原假设拒绝决策对于非均匀和异质一维数据均有效，但簇的 p 值在粗糙/平滑区域中可能分别稍微太小/太大。由于聚类级别的 p 值永远不会超过 alpha，因此对于假设检验而言，这些 p 值误差可以忽略不计。然而，应避免簇间 p 值比较。讨论了统计功效和一般结果解释的含义。 (C) 2019 Elsevier Ltd. 保留所有权利。
Nonuniform (non-constant) temporal smoothness can arise in biomechanical processes like impacts, and heterogeneous smoothness (unequal smoothness across observations) can arise in mechanically diverse comparisons such as padded vs. unpadded impacts, where padded dynamics are generally smoother than unpadded dynamics. It has been reported that statistical parametric mapping's (SPM's) probability values can be invalid for such cases. The purpose of this paper was to clarify the scope of validity for SPM analysis of nonuniformly and heterogeneously smooth one-dimensional (1D) data. We simulated a variety of nonuniformly and heterogeneously smooth Gaussian 1D data over a range of smoothness values, and computed Type I error rates across 10,000 simulation iterations for each smoothness type. Results showed that, in all cases, SPM accurately controlled error at the prescribed alpha = 0.05. Moreover, the distribution of false positives was uniform across time, implying that all regions are equally likely to produce false positives, irrespective of local roughness. We nevertheless show that cluster-level inferences (i.e., p values specific to local regions of significance), while never exceeding alpha (by definition), may be over-or-underestimated by approximately 0.01 for the currently simulated scenarios. We conclude that SPM's null hypothesis rejection decisions are valid for both nonuniform and heterogeneous 1D data, but that clusters' p values may be marginally too small/large in rough/smooth regions, respectively. Since cluster-level p values never exceed alpha, these p value errors are negligible for hypothesis testing purposes. Nevertheless, inter-cluster p value comparisons should be avoided. Implications for statistical power and general results interpretation are discussed. (C) 2019 Elsevier Ltd. All rights reserved.