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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Self-report inventories are objective personality assessments that use multiple-choice items or numbered scales, typically ranging from 1 (strongly disagree) to 5 (strongly agree). They are often called Likert scales after Rensis Likert. These inventories are widely used due to their ease of administration and cost-effectiveness. One of the most prominent examples is the Minnesota Multiphasic Personality Inventory (MMPI), initially developed in the 1940s to assess abnormal personality traits.
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
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Updated: Sep 14, 2025

Computerized Adaptive Testing System of Functional Assessment of Stroke
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基于树的潜在变量模型,用于评估患者报告的结果指标中的差异性项目功能:一个模拟研究.

Olayinka I Arimoro1, Lisa M Lix2, Scott B Patten1

  • 1Department of Community Health Sciences & O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
|July 18, 2025
PubMed
概括

基于递归分区 (PCTree) 的部分信用模型有效地控制了患者报告结果测量 (PROM) 中的差异项目功能 (DIF). 这种统计方法表现出强的性能,特别是在邦费罗尼校正下,确保在异质群体中获得可靠的结果.

关键词:
差异性项目运行方式潜变量模型的潜变量模型.部分信贷模式是一种部分信贷模式.患者报告的结果衡量结果.统计能力 统计能力一种类型I错误的错误.

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科学领域:

  • 心理测量 心理测量 心理测量
  • 统计建模 统计建模
  • 健康 结果 研究 研究 结果

背景情况:

  • 患者报告的结果指标 (PROM) 对于评估健康状况至关重要.
  • 患者反应的异质性可能导致差异性项目功能 (DIF),威胁到PROM的有效性.
  • 现有的基于树的潜在变量模型,如PCTree,需要对DIF检测进行进一步的性能评估.

研究的目的:

  • 评估基于递归分区 (PCTree) 的部分信贷模型的统计特性,用于检测多种PROM中的DIF.
  • 在各种数据分析条件下评估PCTree的性能.

主要方法:

  • 使用计算机模拟来评估PCTree的性能,包括Bonferroni调整和没有Bonferroni调整.
  • 使用I型错误率和统计功率率来评估模型性能.
  • 使用布拉德利的自由标准来对家族I型错误率控制进行了评估.

主要成果:

  • 使用邦费罗尼校正的PCTree在所有模拟条件下有效控制了家族的I型错误率.
  • 在500个或更大的样本大小 (N) 中,统计能力至少达到80%.
  • 随着非DIF相关解释变量的增加,功率下降.

结论:

  • 作为一种在不同患者群体中评估DIF的方法,PCTree显示出前景.
  • 为使用PCTree提供了最佳数据分析条件的建议.