Fisher信息量的分解以指导开发公平且精确的临床预测模型的样本量——第一部分：二分类结局
A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes
AI总结：
本文提出一种基于Fisher信息分解的方法，通过五步流程计算样本量，以开发具有精确个体风险估计的临床预测模型，从而提高公平性和决策可靠性。
摘要：
在开发临床预测模型时，开发数据集的样本量是一个关键考虑因素。小样本量会导致对过拟合、不稳定性、性能不佳和缺乏公平性的更大担忧。先前的研究已概述了最小样本量计算，以最小化过拟合并精确估计总体风险。然而，即使满足这些标准，个体水平风险估计的不确定性（不稳定性）也可能相当大。在本文中，我们提出如何检查和计算开发具有可接受的精确个体水平风险估计的模型所需的样本量，以指导决策并提高公平性。我们概述了一个五步流程，可在数据收集前或当现有数据集可用时使用。它要求研究人员指定目标人群中的总体风险、模型中关键预测因子的（预期）分布，以及一个假定的'核心模型'，该模型可以直接指定（即提供逻辑回归方程）或基于指定的C统计量和（标准化）预测因子的相对效应。我们产生了封闭形式的解，将个体风险估计的方差分解为Fisher单位信息矩阵、预测因子值和总样本量；这使研究人员能够快速计算和检查指定样本量下的个体水平不确定性区间宽度和分类不稳定性。这些信息可以使用预测和分类不稳定性图呈现给关键利益相关者（例如，卫生专业人员、患者、资助者），以帮助确定提高个体预测的信任度、可靠性和公平性所需的（目标）样本量。我们的提议已在软件模块pmstabilityss中实现。我们提供了真实示例，并强调了临床背景的重要性，包括用于决策的任何风险阈值。
英文摘要：
When developing a clinical prediction model, the sample size of the development dataset is a key consideration. Small sample sizes lead to greater concerns of overfitting, instability, poor performance and lack of fairness. Previous research has outlined minimum sample size calculations to minimise overfitting and precisely estimate the overall risk. However even when meeting these criteria, the uncertainty (instability) in individual-level risk estimates may be considerable. In this article we propose how to examine and calculate the sample size required for developing a model with acceptably precise individual-level risk estimates to inform decisions and improve fairness. We outline a five-step process to be used before data collection or when an existing dataset is available. It requires researchers to specify the overall risk in the target population, the (anticipated) distribution of key predictors in the model, and an assumed 'core model' either specified directly (i.e., a logistic regression equation is provided) or based on specified C-statistic and relative effects of (standardised) predictors. We produce closed-form solutions that decompose the variance of an individual's risk estimate into Fisher's unit information matrix, predictor values and total sample size; this allows researchers to quickly calculate and examine individual-level uncertainty interval widths and classification instability for specified sample sizes. Such information can be presented to key stakeholders (e.g., health professionals, patients, funders) using prediction and classification instability plots to help identify the (target) sample size required to improve trust, reliability and fairness in individual predictions. Our proposal is implemented in software module pmstabilityss. We provide real examples and emphasise the importance of clinical context including any risk thresholds for decision making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
