Long questionnaires increase the response burden for patients and healthcare workers. In the treatment of Parkinson's disease, the MDS-UPDRS questionnaire to track disease progression may be underutilized due to time requirements. While reduced item sets have been studied using Fisher information from Item Response Theory (IRT) models, optimal selection methods remain unclear. We compared three methods for selecting an optimal subset of items, with the aim of minimizing the uncertainty in the estimates of the disease severity: Ranking by the Fisher information, coordinate descent local search to directly minimize estimate uncertainty, and adaptive selection. Whereas item ranking based on the expected Fisher information outperformed random choice of items, we saw further gains with the coordinate descent algorithm that directly minimizes the uncertainty of the disease severity estimate. An adaptive algorithm gave an additional slight gain compared to the coordinate descent method. However, the performance of the adaptive method is a best-case limit as we assume that we find the optimal set for the true latent trait scores. For a 5-item subset, the ranked Fisher information method reduced the expected standard deviation by 14 percent compared to random item selection. The corresponding reductions for coordinate descent and adaptive selection were 26 percent and 34 percent respectively. More sophisticated selection methods substantially improved estimate accuracy for small item sets, with diminishing returns for larger subsets. Because item parameters are retained from the full test, reduced item sets measure the same latent construct as the original test. The choice of method entails a trade-off between methodological complexity and precision.

翻译：长问卷会增加患者和医护人员的应答负担。在帕金森病治疗中，用于追踪疾病进展的MDS-UPDRS问卷可能因耗时问题而未被充分利用。尽管已有研究利用项目反应理论（IRT）模型中的费舍尔信息探讨精简条目集，但最优选择方法仍不明确。我们比较了三种选择最优条目子集的方法，旨在最小化疾病严重程度估计的不确定性：基于费舍尔信息排序、通过坐标下降局部搜索直接最小化估计不确定性，以及自适应选择方法。虽然基于期望费舍尔信息排序的条目选择优于随机选择，但直接最小化疾病严重程度估计不确定性的坐标下降算法展现了更大优势。与坐标下降法相比，自适应算法获得了额外的微弱增益。然而，自适应方法的性能是一个最优情况下的上限，这是基于我们假设能针对真实潜在特质分数找到最优集这一前提。对于5条目的子集，与随机条目选择相比，费舍尔信息排序法将期望标准差降低了14%。坐标下降法和自适应选择的相应降幅分别为26%和34%。更精细的选择方法能显著提升小条目集的估计精度，但随着子集增大，收益递减。由于条目参数保留自完整测试，精简条目集测量的是与原测试相同的潜在构念。方法的选择需要在方法复杂性与精度之间进行权衡。