Talkkari Anna, Rosenström Tom H
Department of Psychology, Faculty of Medicine, University of Helsinki, Finland.
Assessment. 2025 Sep;32(6):978-991. doi: 10.1177/10731911241275327. Epub 2024 Sep 9.
Unlike depression sum scores, the underlying risk for depression is typically assumed to be normally distributed across the general population. To assess the true empirical shape of depression risk, we created a continuous-valued estimate of the latent depression density, using the Davidian-Curve Item Response Theory (DC-IRT) and the National Health and Nutrition Examination Survey (NHANES) cohorts from 2005 to 2018 ( = 36,244 on the Nine-item Patient Health Questionnaire; PHQ-9). We conducted simulations to investigate the performance of DC-IRT for large samples and realistic items. The method can recover complex latent-risk distributions even when they are not evident from sum scores. However, estimation accuracy for different sample sizes depends on the method of model selection. In addition to full-data analysis, random samples of a few thousand observations were drawn for analysis. The latent shape of depression was left-skewed and bimodal in both investigations, indicating that the latent-normality assumption does not hold for depression.
与抑郁总分不同，通常认为抑郁的潜在风险在普通人群中呈正态分布。为了评估抑郁风险的真实经验分布形状，我们使用大卫曲线项目反应理论（DC-IRT）和2005年至2018年的美国国家健康和营养检查调查（NHANES）队列（九项患者健康问卷；PHQ-9的样本量为36,244）创建了潜在抑郁密度的连续值估计。我们进行了模拟，以研究DC-IRT在大样本和实际项目中的表现。即使总分中不明显，该方法也能恢复复杂的潜在风险分布。然而，不同样本量的估计准确性取决于模型选择方法。除了全数据分析外，还抽取了数千个观察值的随机样本进行分析。在两项研究中，抑郁的潜在分布形状均为左偏态和双峰，这表明抑郁的潜在正态性假设不成立。
