如何评估顺势疗法预后因素研究的变化?
Lex Rutten1, José E Eizayaga2, Harleen Kaur3
1Independent researcher, Breda, The Netherlands.
概括
在预后因素研究 (PFR) 中对极性症状 (PS) 的统计分析揭示了不对称性. 累积二项概率 (CBP) 有助于对PFR结果的可靠性进行分类,有助于临床决策.
科学领域:
- 顺势疗法研究 研究 顺势疗法研究
- 医学中的统计分析.
- 预测因素研究 (PFR) 是指预测因素的研究.
背景情况:
- 预后因素研究 (PFR) 通常涉及分析极性症状 (PS),这些症状具有相反的值 (例如,改善/欲望与恶化/厌恶).
- 在PS频率分布中观察到的差异,例如"欲望盐"与"厌恶盐"对于*Arsenicum专辑*,引发了关于统计变化的问题.
- 之前的研究和当前的数据收集方法可能导致PS中不对称的频率分布.
研究的目的:
- 将预后因素研究 (PFR) 结果的可靠性分类,特别是对极地症状 (PS).
- 评估统计变异对观察到的PS差异的影响.
- 为了比较累积二项概率 (CBP) 和95%信心区间 (95%CI) 在PFR日常实践中的有用性.
主要方法:
- 对极性症状 (PS) 频率分布的分析和与以前的研究进行比较.
- 计算累积二项概率 (CBP) 和95%信心区间 (95%CI) 以评估统计变化.
- 使用95%CI对CBP进行校准,并通过可比症状对结果进行验证.
主要成果:
- 在几个PS中观察到不对称性,与之前的研究不同,可能是由于使用问卷和不考虑临床专业知识.
- 这种不对称性可能导致"上限效应"和降低概率比率 (LRs).
- CBP与95%的CI相关,为PFR结果提供了一定的统计确定性,这些结果可以被分类为"确定"",可能"",可能"或"可疑".
结论:
- 在PS频率分布中的不对称性可能来自于从业人员在症状评估过程中不充分利用专家知识.
- 基于CBP的可靠性分类更具信息性,并且更好地被顺势疗法专家理解,尽管它仍然部分主观.
- 对结果的验证和临床判断对于确定PFR发现的临床有效性至关重要;建议研究人员和从业人员接受统计学和顺势疗法的培训.
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