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.
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.
| Original language | English |
|---|---|
| Article number | dyad082 |
| Pages (from-to) | 1545–1556 |
| Number of pages | 12 |
| Journal | International Journal of Epidemiology |
| Volume | 52 |
| Issue number | 5 |
| Early online date | 19 Jun 2023 |
| DOIs | |
| Publication status | Published - 1 Oct 2023 |
Bibliographical note
Funding Information:This work was supported by the UK Medical Research Council (grant numbers MR/N013638/1 PhD studentship to C.E.R., MR/P014054/1 Skills Development Fellowship to M.C.B., MC_UU_00011/1&6); British Heart Foundation (CH/F/20/90003 and AA/18/7/34219); National Institute of Health Research Senior Investigator Award (NF-0616–10102 to D.A.L.); and University of Bristol Vice-Chancellor’s Fellowships to L.A.C.M. and M.C.B. All authors work in or are affiliated with a unit that receives support from the University of Bristol and UK Medical Research Council. Acknowledgements
Publisher Copyright:
© 2023 The Author(s). Published by Oxford University Press on behalf of the International Epidemiological Association.