Pharmacometrics Research Group, Department of Pharmacy, Uppsala University, Box 580, 751 23, Uppsala, Sweden.
AAPS J. 2021 Mar 16;23(3):45. doi: 10.1208/s12248-021-00555-3.
Composite scale data is widely used in many therapeutic areas and consists of several categorical questions/items that are usually summarized into a total score (TS). Such data is discrete and bounded by nature. The gold standard to analyse composite scale data is item response theory (IRT) models. However, IRT models require item-level data while sometimes only TS is available. This work investigates models for TS. When an IRT model exists, it can be used to derive the information as well as expected mean and variability of TS at any point, which can inform TS-analyses. We propose a new method: IRT-informed functions of expected values and standard deviation in TS-analyses. The most common models for TS-analyses are continuous variable (CV) models, while bounded integer (BI) models offer an alternative that respects scale boundaries and the nature of TS data. We investigate the method in CV and BI models on both simulated and real data. Both CV and BI models were improved in fit by IRT-informed disease progression, which allows modellers to precisely and accurately find the corresponding latent variable parameters, and IRT-informed SD, which allows deviations from homoscedasticity. The methodology provides a formal way to link IRT models and TS models, and to compare the relative information of different model types. Also, joint analyses of item-level data and TS data are made possible. Thus, IRT-informed functions can facilitate total score analysis and allow a quantitative analysis of relative merits of different analysis methods.
组合量表数据在许多治疗领域中被广泛应用，它由几个类别问题/项目组成，通常汇总为一个总分（TS）。这种数据是离散的，本质上是有界的。分析组合量表数据的金标准是项目反应理论（IRT）模型。然而，IRT 模型需要项目级别的数据，而有时只有 TS 可用。这项工作研究了 TS 的模型。当存在 IRT 模型时，可以使用它来推导出任何一点 TS 的信息以及预期均值和方差，这可以为 TS 分析提供信息。我们提出了一种新的方法：在 TS 分析中使用 IRT 推断的期望函数和标准偏差。最常见的 TS 分析模型是连续变量（CV）模型，而有界整数（BI）模型提供了一种替代方法，它尊重量表边界和 TS 数据的性质。我们在模拟数据和真实数据上研究了 CV 和 BI 模型中的方法。IRT 推断的疾病进展改善了 CV 和 BI 模型的拟合度，这使得建模者能够精确而准确地找到相应的潜在变量参数，以及 IRT 推断的 SD，这允许偏离同方差性。该方法提供了一种正式的方法来连接 IRT 模型和 TS 模型，并比较不同模型类型的相对信息。此外，还可以对项目级别的数据和 TS 数据进行联合分析。因此，IRT 推断的函数可以促进总分分析，并允许对不同分析方法的相对优点进行定量分析。
