D'Angelo Nicoletta
Department of Economics, Business, and Statistics, University of Palermo, Palermo, Italy.
Stat Med. 2025 Jun;44(13-14):e70150. doi: 10.1002/sim.70150.
This paper introduces a new method for change detection in medical and psychometric studies based on the recently introduced pseudo Score statistic, for which the sampling distribution under the alternative hypothesis has been determined. Our approach has the advantage of simplicity in its computation, eliminating the need for resampling or simulations to obtain critical values. Additionally, it comes with known null and alternative distributions, facilitating easy calculations for power levels and sample size planning. The paper indeed also discusses the topic of power analysis in segmented regression, namely the estimation of sample size or power level when the study data being collected focuses on a covariate expected to affect the mean response via a piecewise relationship with an unknown breakpoint. We run simulation studies showing that our method outperforms other Tests for a Change Point (TFCP) with both normally distributed and binary data and carry out two real data analyses on genomic data and SAT critical reading data. The proposed test contributes to the framework of medical and psychometric research, and it is available on the Comprehensive R Archive Network (CRAN) and in a more user-friendly Shiny App, both illustrated at the end of the paper.
本文介绍了一种基于最近引入的伪得分统计量的医学和心理测量学研究中的变化检测新方法，其备择假设下的抽样分布已确定。我们的方法在计算上具有简单的优点，无需重采样或模拟来获得临界值。此外，它具有已知的原假设和备择分布，便于进行功效水平和样本量规划的轻松计算。本文还确实讨论了分段回归中的功效分析主题，即在收集的研究数据集中于预期通过与未知断点的分段关系影响平均反应的协变量时，样本量或功效水平的估计。我们进行了模拟研究，表明我们的方法在正态分布和二元数据方面均优于其他变化点检验（TFCP），并对基因组数据和SAT批判性阅读数据进行了两次实际数据分析。所提出的检验为医学和心理测量学研究框架做出了贡献，它可在综合R存档网络（CRAN）上获取，并且在一个更用户友好的Shiny应用程序中也可获取，两者均在本文末尾进行了说明。
