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对开发具有二进制结果的风险预测模型的样本大小要求的评估
Menelaos Pavlou1, Gareth Ambler2, Chen Qu2
1Department of Statistical Science, UCL, London, UK. m.pavlou@ucl.ac.uk.
BMC medical research methodology
|July 10, 2024
概括
风险预测模型中现有的样本大小计算公式对于高模型强度是不可靠的. 建议采用新的基于模拟的方法来准确估计样本大小,以改善临床决策.
科学领域:
- 生物统计学 生物统计学
- 临床流行病学临床流行病学
- 医疗信息学 医疗信息学
背景情况:
- 风险预测模型对于临床决策至关重要.
- 小样本大小可能会损害模型性能和通用性.
- 校准斜率 (CS) 和平均绝对预测误差 (MAPE) 是样本大小计算的关键指标.
研究的目的:
- 评估风险预测模型现有样本大小计算公式的性能.
- 在各种条件下评估这些公式的准确性,包括不同的模型强度和结果流行率.
- 在临床风险预测中提出一个改进的样本大小估计方法.
主要方法:
- 进行了一项模拟研究,以评估两个拟议的样本大小计算公式.
- 该研究分析了公式的性能,基于预期的数据特征,如结果流行率和c-statistic.
- 评估考虑了二进制结果和时间到事件数据与审查.
主要成果:
- 现有的公式对强度较低的模型 (c-统计值<0.8) 具有充分的性能.
- CS公式低估了高模型强度 (c-统计值>0.8) 所需的样本大小,因此需要增加50-100%.
- MAPE公式倾向于高模型强度的样本大小过大估计,在更高的结果流行率下,效应更明显.
结论:
- 目前的样本大小公式通常适用于较低的模型强度,但偏向于临床环境中常见的较高强度.
- 在R包"samplesizedev"中实施的一种基于模拟的新方法被提议用于准确的样本大小估计.
- 拟议的方法通过计算CS和MAPE的变化来考虑模型稳定性.
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