Vector Psychometric Group, LLC, 847 Emily Lane, Chapel Hill, NC, 27516, USA.
YourCareChoice, Ann Arbor, MI, USA.
Qual Life Res. 2018 Jul;27(7):1721-1734. doi: 10.1007/s11136-018-1801-z. Epub 2018 Feb 8.
Measurement development in hard-to-reach populations can pose methodological challenges. Item response theory (IRT) is a useful statistical tool, but often requires large samples. We describe the use of longitudinal IRT models as a pragmatic approach to instrument development when large samples are not feasible.
The statistical foundations and practical benefits of longitudinal IRT models are briefly described. Results from a simulation study are reported to demonstrate the model's ability to recover the generating measurement structure and parameters using a range of sample sizes, number of items, and number of time points. An example using early-phase clinical trial data in a rare condition demonstrates these methods in practice.
Simulation study results demonstrate that the longitudinal IRT model's ability to recover the generating parameters rests largely on the interaction between sample size and the number of time points. Overall, the model performs well even in small samples provided a sufficient number of time points are available. The clinical trial data example demonstrates that by using conditional, longitudinal IRT models researchers can obtain stable estimates of psychometric characteristics from samples typically considered too small for rigorous psychometric modeling.
Capitalizing on repeated measurements, it is possible to estimate psychometric characteristics for an assessment even when sample size is small. This allows researchers to optimize study designs and have increased confidence in subsequent comparisons using scores obtained from such models. While there are limitations and caveats to consider when using these models, longitudinal IRT modeling may be especially beneficial when developing measures for rare conditions and diseases in difficult-to-reach populations.
在难以接触的人群中进行测量开发可能会带来方法学上的挑战。项目反应理论（IRT）是一种有用的统计工具，但通常需要大量样本。我们描述了在无法获得大样本时，使用纵向 IRT 模型作为仪器开发的实用方法。
简要描述了纵向 IRT 模型的统计基础和实际优势。报告了一项模拟研究的结果，以展示该模型在使用一系列样本量、项目数量和时间点数量时，恢复生成测量结构和参数的能力。在罕见疾病的早期临床试验数据中，通过一个示例说明了这些方法在实践中的应用。
模拟研究结果表明，纵向 IRT 模型恢复生成参数的能力在很大程度上取决于样本量和时间点数量之间的相互作用。总体而言，即使在小样本量的情况下，只要有足够数量的时间点，该模型的性能也很好。临床试验数据示例表明，通过使用条件性纵向 IRT 模型，研究人员可以从通常被认为太小而无法进行严格心理计量建模的样本中获得心理计量特征的稳定估计。
通过重复测量，可以在样本量较小时估算评估的心理计量特征。这使得研究人员能够优化研究设计，并在使用这些模型获得的分数进行后续比较时更有信心。虽然在使用这些模型时存在限制和注意事项，但在为难以接触的人群中的罕见疾病和疾病开发测量工具时，纵向 IRT 建模可能特别有益。
