Item parameter estimation in pharmacometric item response theory (IRT) models is predominantly performed using the Laplace estimation algorithm as implemented in NONMEM. In psychometrics a wide range of different software tools, including several packages for the open-source software R for implementation of IRT are also available. Each have their own set of benefits and limitations and to date a systematic comparison of the primary estimation algorithms has not been evaluated. A simulation study evaluating varying number of hypothetical sample sizes and item scenarios at baseline was performed using both Laplace and Gauss-hermite quadrature (GHQ-EM). In scenarios with at least 20 items and more than 100 subjects, item parameters were estimated with good precision and were similar between estimation algorithms as demonstrated by several measures of bias and precision. The minimal differences observed for certain parameters or sample size scenarios were reduced when translating to the total score scale. The ease of use, speed of estimation and relative accuracy of the GHQ-EM method employed in mirt make it an appropriate alternative or supportive analytical approach to NONMEM for potential pharmacometrics IRT applications.

翻译：药代动力学项目反应理论（IRT）模型中的项目参数估计主要采用NONMEM中实现的拉普拉斯估计算法。在心理测量学领域，存在多种不同的软件工具，包括开源软件R中用于实现IRT的若干程序包。每种方法皆有其独特的优势与局限，迄今为止尚未对主要估计算法进行系统比较。本研究通过模拟实验，在基线条件下评估不同假设样本量与项目情境，同时采用拉普拉斯算法和高斯-埃尔米特求积法（GHQ-EM）进行对比分析。在至少包含20个项目且样本量超过100名受试者的情境中，项目参数估计展现出良好的精确度，且通过多种偏差与精度指标验证，两种估计算法所得结果具有高度一致性。当转换至总分尺度时，特定参数或样本量情境中观察到的微小差异进一步缩小。mirt软件包采用的GHQ-EM方法兼具易用性、快速估计特性及相对准确性，使其成为NONMEM在潜在药代动力学IRT应用中的合适替代或辅助分析方法。