期刊
BIOMETRICAL JOURNAL
卷 63, 期 8, 页码 1652-1672出版社
WILEY
DOI: 10.1002/bimj.202100046
关键词
latent variable modeling; measurement invariance; multilevel analysis; patient-reported outcomes; structural equation modeling
资金
- Emotional BrainBV
In this paper, data from a clinical trial studying the impact of an on-demand drug on women with low sexual desire were analyzed. The traditional approach of aggregating data before analysis was found to have drawbacks, such as varying standard errors and implicit assumptions about item weights. A multilevel structural equation model was proposed as an alternative analysis method to address these issues.
We analyze data from a clinical trial investigating the effect of an on-demand drug for women with low sexual desire. These data consist of a varying number of measurements/events across patients of when the drug was taken, including data on a patient-reported outcome consisting of five items measuring an unobserved construct (latent variable). Traditionally, these data are aggregated prior to analysis by composing one sum score per event and averaging this sum score over all observed events. In this paper, we explain the drawbacks of this aggregating approach. One drawback is that these averages have different standard errors because the variance of the underlying events differs between patients and because the number of events per patient differs. Another drawback is the implicit assumption that all items have equal weight in relation to the latent variable being measured. We propose a multilevel structural equation model, treating the events (level 1) as nested observations within patients (level 2), as alternative analysis method to overcome these drawbacks. The model we apply includes a factor model measuring a latent variable at the level of the event and at the level of the patient. Then, in the same model, the latent variables are regressed on covariates to assess the drug effect. We discuss the inferences obtained about the efficacy of the on-demand drug using our proposed model. We further illustrate how to test for measurement invariance across grouping covariates and levels using the same model.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据