Exploring regression dilution bias using repeat measurements of 2858 variables in ≤49 000 UK Biobank participants

Abstract

Background 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. Methods 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. Results 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. Conclusions 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.

Association of red cell distribution width to albumin ratio with risk of all-cause and cause-specific mortality: two prospective cohort studies

“…Serum albumin is an important marker of nutritional status and inflammatory response[11, 12]. Previous studies have reported that higher RDW[35–38] and lower albumin[14–16] were both associated with increased risk of incident chronic diseases and mortality in the general population. Therefore, we could hypothesis that the combined of the two markers could simultaneously reflect the inflammation and nutritional status in lifespan, and then related to all-cause and cause-specific mortality.…”

Ratio of Red Blood Cell Distribution Width to Albumin Level and Risk of Mortality

“…Serum albumin concentration is an important marker of nutritional status and the inflammatory response . Previous studies have reported that greater RDW and lower serum albumin concentration are both associated with an increased risk of incident chronic disease and mortality in the general population. Therefore, we hypothesized that the integration of these 2 markers may reflect inflammation, malnutrition, and other abnormalities throughout the lifespan and could be associated with all-cause and cause-specific mortality.…”

Self-report inaccuracy in the UK Biobank: Impact on inference and interplay with selective participation

“…In summary, our findings emphasize that both self-report data quality and sampling features are potential sources of poor reproducibility for biobank-scale research, leading to imprecision and bias that can complicate the interpretation of findings. Analogous to quality control procedures developed for the processing of genetic data, the application of tools designed to enhance phenotype resolution (e.g., repeat measurements, regression calibration 16 , imputation 31 , weighted regression) and sample representativeness (e.g., probability sampling or weighting) should therefore become an integral part of data collection, pre-analytic data handling and sensitivity checks.…”

“…However, with the increasing availability of repeated measurements in the UKBB, it is now possible to more systematically explore causes and consequences of self-report inconsistencies across measurement occasions. In this work, we aim to contribute to the growing body of research scrutinizing the impact of study design characteristics and participant behaviour (e.g., sampling procedures [9][10][11] , missing data 12 , study engagement 13 , data quality [14][15][16] ) on findings obtained from biobankscale data. Here, we focus on the challenges related to reporting error, defined as inconsistent self-reporting across measurement occasions.…”