Zhao Chong-Ye, Yan Min-Qian, Xu Xiao-Han, Ou Chun-Quan
Department of Biostatistics, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China.
Qual Life Res. 2025 Feb 19. doi: 10.1007/s11136-025-03924-z.
Calculating the threshold for meaningful within-individual change (MWIC) is essential for interpreting patient-reported outcomes (PRO). However, traditional methods of determining MWIC threshold yield varying estimates and lack a standardized approach. We aim to propose a novel method for more accurate MWIC threshold estimation.
We developed a weighted predictive modeling method. The weighting involved using the rank difference between PRO score change and the anchor of each individual. A Monte Carlo simulation was conducted to compare the performance of the new method and that of existing state-of-the-art methods. Simulation parameters included distributions of PRO score changes, sample sizes, improvement proportions, and correlation strengths. Statistical performance was assessed using relative bias (rbias), coefficient of variation (CV), and relative root mean squared error (rRMSE).
Distribution-based methods had the largest rbias and rRMSE among all methods. Existing anchor-based methods except for the Terluin 2022 method were biased when the correlation strength was weak or when the improvement proportion was not 50%. The Terluin 2022 method requires estimating an important reliability parameter, and this method had highest CV compared to other predictive modeling methods. The new weighted method demonstrated the smallest rRMSE across most simulation settings. It also maintained relatively high accuracy under weak correlation strength or imbalanced improvement proportion. Similar results were presented under normal or skewed distributions of PRO score changes.
This novel method offers a simple and feasible alternative to existing predictive modeling methods for estimating MWIC threshold, which can facilitate the application of PRO.
计算有意义的个体内变化（MWIC）阈值对于解释患者报告结局（PRO）至关重要。然而，传统的确定MWIC阈值的方法会产生不同的估计值，且缺乏标准化方法。我们旨在提出一种更准确地估计MWIC阈值的新方法。
我们开发了一种加权预测建模方法。加权涉及使用PRO评分变化与每个个体的锚定指标之间的秩差。进行了蒙特卡罗模拟，以比较新方法与现有最先进方法的性能。模拟参数包括PRO评分变化的分布、样本量、改善比例和相关强度。使用相对偏差（rbias）、变异系数（CV）和相对均方根误差（rRMSE）评估统计性能。
在所有方法中，基于分布的方法具有最大的rbias和rRMSE。当相关强度较弱或改善比例不是50%时，除Terluin 2022方法外的现有基于锚定指标的方法存在偏差。Terluin 2022方法需要估计一个重要的可靠性参数，与其他预测建模方法相比，该方法的CV最高。新的加权方法在大多数模拟设置中表现出最小的rRMSE。在弱相关强度或改善比例不均衡的情况下，它也保持了相对较高的准确性。在PRO评分变化的正态或偏态分布下也呈现出类似结果。
这种新方法为估计MWIC阈值提供了一种简单可行的替代现有预测建模方法的方法，有助于PRO的应用。
