Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway.
Int J Epidemiol. 2019 Oct 1;48(5):1721-1726. doi: 10.1093/ije/dyz055.
Using a continuous exposure variable that is measured with random error in a univariable linear regression model leads to regression dilution bias: the observed association between the exposure and outcome is smaller than it would be if the true value of the exposure could be used. A repeatability sub-study, where a sample of study participants have their data measured again, can be used to correct for this bias. It is important to perform a sample size calculation for such a sub-study, to ensure that correction factors can be estimated with sufficient precision. We describe how a previously published method can be used to calculate the sample size from the anticipated size of the correction factor and its desired precision, and demonstrate this approach using the example of the cross-sectional studies conducted as part of the International Project on Cardiovascular Disease in Russia study. We also provide correction factors calculated from repeat data from the UK Biobank study, which can be used to help plan future repeatability studies.
在单变量线性回归模型中，使用具有随机误差的连续暴露变量会导致回归稀释偏差：暴露与结果之间的观察到的关联比如果可以使用暴露的真实值时要小。重复性子研究可以用于纠正这种偏差，其中研究参与者的样本再次测量其数据。对于此类子研究，进行样本量计算非常重要，以确保可以以足够的精度估计校正因子。我们描述了如何使用先前发表的方法根据预期的校正因子大小及其所需的精度来计算样本量，并使用作为俄罗斯心血管疾病国际项目一部分进行的横断面研究的示例演示了这种方法。我们还提供了从英国生物库研究的重复数据中计算出的校正因子，这些因子可用于帮助计划未来的可重复性研究。
