MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Department of Medical Statistics, London School of Hygiene and Tropical Medicine (LSHTM), London, UK.
Int J Epidemiol. 2023 Oct 5;52(5):1545-1556. doi: 10.1093/ije/dyad082.
Measurement error in exposures and confounders can bias exposure-outcome associations but is rarely considered. We aimed to assess random measurement error of all continuous variables in UK Biobank and explore approaches to mitigate its impact on exposure-outcome associations.
Random measurement error was assessed using intraclass correlation coefficients (ICCs) for all continuous variables with repeat measures. Regression calibration was used to correct for random error in exposures and confounders, using the associations of red blood cell distribution width (RDW), C-reactive protein (CRP) and 25-hydroxyvitamin D [25(OH)D] with mortality as illustrative examples.
The 2858 continuous variables with repeat measures varied in sample size from 109 to 49 121. They fell into three groups: (i) baseline visit [529 variables; median (interquartile range) ICC = 0.64 (0.57, 0.83)]; (ii) online diet by 24-h recall [22 variables; 0.35 (0.30, 0.40)] and (iii) imaging measures [2307 variables; 0.85 (0.73, 0.94)]. Highest ICCs were for anthropometric and medical history measures, and lowest for dietary and heart magnetic resonance imaging.The ICCs (95% confidence interval) for RDW, CRP and 25(OH)D were 0.52 (0.51, 0.53), 0.29 (0.27, 0.30) and 0.55 (0.54, 0.56), respectively. Higher RDW and levels of CRP were associated with higher risk of all-cause mortality, and higher concentration of 25(OH)D with lower risk. After correction for random measurement error in the main exposure, the associations all strengthened. Confounder correction did not influence estimates.
Random measurement error varies widely and is often non-negligible. For UK Biobank we provide relevant statistics and adaptable code to help other researchers explore and correct for this.
暴露因素和混杂因素的测量误差会使暴露与结局之间的关联产生偏倚，但这一点很少被考虑到。我们旨在评估英国生物库中所有连续变量的随机测量误差，并探索减轻其对暴露与结局关联影响的方法。
采用组内相关系数（ICC）评估所有具有重复测量的连续变量的随机测量误差。使用红细胞分布宽度（RDW）、C 反应蛋白（CRP）和 25-羟维生素 D [25(OH)D]与死亡率的关联作为说明性实例，通过回归校准来校正暴露因素和混杂因素的随机误差。
具有重复测量的 2858 个连续变量的样本量从 109 到 49121 不等。它们分为三组：（i）基线访视[529 个变量；中位数（四分位距）ICC=0.64（0.57，0.83）]；（ii）通过 24 小时回忆的在线饮食[22 个变量；0.35（0.30，0.40）]和（iii）成像测量[2307 个变量；0.85（0.73，0.94）]。最高的 ICC 见于人体测量和病史测量，最低的见于饮食和心脏磁共振成像。RDW、CRP 和 25(OH)D 的 ICC（95%置信区间）分别为 0.52（0.51，0.53）、0.29（0.27，0.30）和 0.55（0.54，0.56）。较高的 RDW 和 CRP 水平与全因死亡率的风险增加相关，而较高的 25(OH)D 浓度与较低的风险相关。在校正主要暴露因素的随机测量误差后，所有关联均得到增强。混杂因素校正不影响估计值。
随机测量误差差异很大，且通常不可忽略。对于英国生物库，我们提供了相关的统计数据和可适应的代码，以帮助其他研究人员探索和校正这种误差。
