ArticlePloS one2020
Recycling of predictors used to estimate glomerular filtration rate: Insight into lateral collinearity.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Lamivudine and tenofovir pharmacokinetic variability in people with HIV in Papua New Guinea.British journal of clinical pharmacology · 2025Article
- Longitudinal analysis of serum urate in prediabetic phase.Rheumatology (Oxford, England) · 2024Article
- Age and the eGFR-dependent risk for adverse clinical outcomes.Clinical kidney journal · 2023Review
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2 authors.
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Abstract
backgroundOne overlooked problem in statistical analysis is lateral collinearity, a phenomenon that may occur when the outcome variable derives from the predictors. In nephrology this issue is seen with the use of estimated glomerular filtration rate (eGFR) as an outcome and age, sex, and ethnicity as predictors. In this study with simulated data, we aim to illustrate this problem.
methodsWe randomly generated unrelated data to estimate eGFR by common equations.
resultsUsing simulated data, we show that age, gender, and ethnicity (recycled predictors variables) are statistically significantly correlated with eGFR in linear regression analysis. Whereas the initial obvious conclusion is that age, sex, and ethnicity are strong predictors of eGFR, more rigorous interpretation suggests that this is a byproduct of the mathematical model produced when deriving new predictors from another.
conclusionWhile statistical models have the ability to identify vertical collinearity (predictor-predictor), lateral collinearity (predictor-outcome) is seldom identified and discussed in statistical analysis. Therefore, caution is needed when interpreting the correlation between age, gender, and ethnicity with eGFR derived from regression analyses.
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