Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes
Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes
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DOI：
10.1002/sim.7993
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发表时间：
2019-03-30
影响因子：
2
通讯作者：
Collins, Gary S.
中科院分区：
文献类型：
--
作者：
Riley, Richard D.;Snell, Kym I. E.;Collins, Gary S.
在医学文献中，正在开发数百种预测模型来预测个人的健康结果。对于连续的结果，通常建立一个线性回归模型，以多个预测因子（协变量）的值为条件来预测个体的结果值。为了改进模型开发并减少过度拟合的可能性，需要一个合适的样本量，即受试者的数量(n)相对于潜在纳入的预测参数的数量(p)。我们建议n的最小值应满足以下四个关键标准：(i)预测器效应估计的小乐观度，由全局收缩因子>= 0.9定义；的绝对差很小
In the medical literature, hundreds of prediction models are being developed to predict health outcomes in individuals. For continuous outcomes, typically a linear regression model is developed to predict an individual's outcome value conditional on values of multiple predictors (covariates). To improve model development and reduce the potential for overfitting, a suitable sample size is required in terms of the number of subjects (n) relative to the number of predictor parameters (p) for potential inclusion. We propose that the minimum value of n should meet the following four key criteria: (i) small optimism in predictor effect estimates as defined by a global shrinkage factor of >= 0.9; (ii) small absolute difference of