Byrne David, Boland Fiona, Brannick Susan, Carney Robert M, Cuijpers Pim, Dima Alexandra L, Freedland Kenneth E, Guerin Suzanne, Hevey David, Kathuria Bishember, Wallace Emma, Doyle Frank
School of Population Health, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
School of Population Health, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
J Clin Epidemiol. 2025 Jul;183:111762. doi: 10.1016/j.jclinepi.2025.111762. Epub 2025 Mar 24.
As multiple sophisticated techniques are used to evaluate psychometric scales, in theory reducing error and enhancing the measurement of patient-reported outcomes, we aimed to determine whether applying different psychometric analyses would demonstrate important differences in treatment effects.
We conducted a secondary analysis of individual participant data (IPD) from 20 antidepressant treatment trials obtained from Vivli.org (n = 6843). Pooled item-level data from the Hamilton Rating Scale for Depression (HRSD-17) were analyzed using confirmatory factory analysis (CFA), item response theory (IRT), and network analysis (NA). Multilevel models were used to analyze differences in trial effects at approximately 8 weeks (range 4-12 weeks) post-treatment commencement, with standardized mean differences calculated as Cohen's d. The effect size outcomes for the original total depression scores were compared with psychometrically informed outcomes based on abbreviated and weighted depression scores.
Several items performed poorly during psychometric analyses and were eliminated, resulting in different models being obtained for each approach. Treatment effects were modified as follows per psychometric approach: 10.4%-14.9% increase for CFA, 0%-2.9% increase for IRT, and 14.9%-16.4% reduction for NA.
Psychometric analyses differentially moderate effect size outcomes depending on the method used. In a 20-trial sample, factor analytic approaches increased treatment effect sizes relative to the original outcomes, NA decreased them, and IRT results reflected original trial outcomes.
This study aimed to determine if using advanced psychometrics methods would inform any clinically or statistically important differences in clinical trial outcomes when compared to original findings. We applied factor analysis (FA), item response theory (IRT), and network analysis (NA) to the most commonly used measure of depression in clinical settings - the Hamilton Rating Scale for Depression (HRSD) - to identify and remove nonperforming survey items and calculate weighted item scores. We found that the efficacy reported in trials increased when using FA to removed items, but decreased when using NA. There was almost no change in efficacy when using IRT. Using weighted scores based on respective models offered no additional utility in terms of increasing or decreasing efficacy outcomes.
由于多种复杂技术被用于评估心理测量量表，理论上可减少误差并增强对患者报告结局的测量，我们旨在确定应用不同的心理测量分析方法是否会在治疗效果上显示出重要差异。
我们对从Vivli.org获取的20项抗抑郁治疗试验的个体参与者数据（IPD）进行了二次分析（n = 6843）。使用验证性因子分析（CFA）、项目反应理论（IRT）和网络分析（NA）对汉密尔顿抑郁量表（HRSD - 17）的汇总项目级数据进行分析。使用多级模型分析治疗开始后约8周（范围4 - 12周）试验效果的差异，标准化平均差异计算为科恩d值。将原始总抑郁评分的效应大小结果与基于简化和加权抑郁评分的心理测量学知情结果进行比较。
几个项目在心理测量分析中表现不佳并被剔除，导致每种方法获得不同的模型。每种心理测量方法对治疗效果的修正如下：CFA增加10.4% - 14.9%，IRT增加0% - 2.9%，NA减少14.9% - 16.4%。
心理测量分析根据所使用的方法对效应大小结果有不同程度的调节作用。在一个包含20项试验的样本中，因子分析方法相对于原始结果增加了治疗效应大小，NA则降低了它们，而IRT结果反映了原始试验结果。
本研究旨在确定与原始结果相比，使用先进的心理测量方法是否会在临床试验结果中揭示任何临床或统计学上的重要差异。我们将因子分析（FA）、项目反应理论（IRT）和网络分析（NA）应用于临床环境中最常用的抑郁测量工具——汉密尔顿抑郁量表（HRSD），以识别和剔除表现不佳的调查项目并计算加权项目得分。我们发现，使用FA剔除项目时试验报告的疗效增加，但使用NA时疗效降低。使用IRT时疗效几乎没有变化。基于各自模型使用加权分数在提高或降低疗效结果方面没有额外的作用。
