Overview over ADE (from ADEMP) table.

The integration of machine learning methodologies has become prevalent in the development of clinical prediction models, often suggesting superior performance compared to traditional statistical techniques. Within the scope of low-dimensional datasets, encompassing both classical and machine learning paradigms, we plan to undertake a comparison of variable selection methodologies through simulation-based analysis. The principal aim is the comparison of the variable selection strategies with respect to relative predictive accuracy and its variability, with a secondary aim the comparison of descriptive accuracy. We use six distinct statistical learning approaches across both data generation and model learning. The present manuscript is a protocol for the corresponding simulation study registration (Study registration Open Science Framework ID: k6c8f). We describe the planned steps through the Aims, Data, Estimands, Methods, and Performance framework for simulation study design and reporting.

机器学习方法在临床预测模型的开发中已日益普及，其性能往往优于传统统计技术。在涵盖经典统计与机器学习范式的低维数据集范畴内，本研究拟通过基于模拟的分析，对各类变量选择方法展开对比研究。本研究的核心目标为对比不同变量选择策略的相对预测准确性及其变异程度，次要目标则为对比其描述性准确性。本研究将在数据生成与模型学习两个阶段，采用六种不同的统计学习方法。本文为对应模拟研究的注册方案（研究注册开放科学框架（Open Science Framework）ID：k6c8f）。我们将按照模拟研究设计与报告的研究目标（Aims）、数据（Data）、估计目标（Estimands）、方法（Methods）与性能（Performance）框架，对拟开展的研究步骤进行详细阐述。