Dataset for: Bayesian monotonic errors-in-variables models with applications to pathogen susceptibility testing

Drug dilution (MIC) and disk diffusion (DIA) are the two most common antimicrobial susceptibility assays used by hospitals and clinics to determine an unknown pathogen's susceptibility to various antibiotics. Since only one assay is commonly used, it is important that the two assays give similar results. Calibration of the DIA assay to the MIC assay is typically done using the error-rate bounded method, which selects DIA breakpoints that minimize the observed discrepancies between the two assays. In 2000, Craig proposed a model-based approach that specifically models the measurement error and rounding processes of each assay, the underlying pathogen distribution, and the true monotonic relationship between the two assays. The two assays are then calibrated by focusing on matching the probabilities of correct classification (susceptible, indeterminant, and resistant). This approach results in greater precision and accuracy for estimating DIA breakpoints. In this paper, we expand the flexibility of the model-based method by introducing a Bayesian four-parameter logistic model (extending Craig's original three-parameter model) as well as a Bayesian nonparametric spline model to describe the relationship between the two assays. We propose two ways to handle spline knot selection, considering many equally-spaced knots but restricting overfitting via a random walk prior and treating the number and location of knots as additional unknown parameters. We demonstrate the two approaches via a series of simulation studies and apply the methods to two real data sets.

药物稀释法（最低抑菌浓度，Minimum Inhibitory Concentration, MIC）与纸片扩散法（disk diffusion, DIA）是医院及临床用于判定未知病原体对各类抗生素敏感性的两种最常用抗菌药物敏感性试验（antimicrobial susceptibility assays）。由于临床通常仅会使用其中一种检测方法，因此确保两种方法的检测结果具有一致性至关重要。将DIA法校准至MIC法通常采用误差率边界法（error-rate bounded method），该方法通过筛选DIA临界值以最小化两种方法间的观测偏差。2000年，Craig提出了一种基于模型的方法，该方法专门对每种检测的测量误差与修约过程、潜在病原体分布以及两种方法间的真实单调关系进行建模。随后该方法通过匹配正确分类（敏感、中介与耐药）的概率完成两种检测方法的校准，可提升DIA临界值估计的精密度与准确性。本文中，我们通过引入贝叶斯四参数逻辑斯蒂模型（Bayesian four-parameter logistic model，拓展了Craig最初的三参数模型）以及贝叶斯非参数样条模型（Bayesian nonparametric spline model）来描述两种检测方法间的关联，以此提升该基于模型方法的灵活性。针对样条结点选择问题，我们提出两种处理方案：一是设置大量等间距结点，并通过随机游走先验（random walk prior）限制过拟合（overfitting）；二是将结点数量与位置视为额外的未知参数。我们通过一系列仿真研究验证了这两种方案，并将其应用于两个真实数据集。