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优化样本大小和统计方法用于深度大脑刺激中的概率甜点映射

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    确定最佳样本大小和统计方法是对帕金森病 (PD) 中可靠的深度大脑刺激 (DBS) 概率绘制的关键. 用14-18名患者的贝叶斯式t测试确保了稳定和一致的结果,以获得更好的临床应用.

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    科学领域:

    • 神经外科 神经外科
    • 神经学 神经学
    • 生物统计学 生物统计学

    背景情况:

    • 深度大脑刺激 (DBS) 使用概率绘制来治疗运动障碍.
    • 由于数据和统计方法的变化,当前的概率绘制方法面临普遍性挑战.
    • 建立最低样本大小和一致的统计方法对于可靠的概率绘制至关重要.

    研究的目的:

    • 确定在DBS中稳定概率映射结果所需的最小样本大小.
    • 确定在概率绘制结果中产生最高一致性的统计方法.
    • 提高概率绘制在运动障碍治疗中的临床相关性和通用性.

    主要方法:

    • 分析了36名接受DBS手术的帕金森病患者的手术内刺激数据.
    • 概率的甜点 (PSS) 计算使用样本大小从4到36名患者.
    • 统计方法包括贝叶斯式t测试,维尔科克森测试与FDR校正,和维尔科克森测试与排列校正,与10次重复.

    主要成果:

    • 贝叶斯式t测试在所有测试指标上始终产生稳定的PSS.
    • 稳定的PSS大小和心脏位点在至少14名患者中实现.
    • 在18名患者中达到了PSS体积 (迪斯系数) 稳定性;贝叶斯式t测试显示出优异的一致性,特别是在较小的样本大小.

    结论:

    • 贝叶斯式t试验是计算DBS中稳定的PSS最合适的方法,特别是在小样本大小的情况下.
    • 建议在帕金森病中采用至少14-18名患者的样本大小来进行可靠的概率测绘.
    • 这些发现对于提高概率绘制的可靠性和临床适用性,改进诊断和优化干预措施至关重要.