de Andrade, Luis Gustavo Modelli [UNESP]Tedesco-Silva, Helio2020-12-122020-12-122020-02-01PLoS ONE, v. 15, n. 2, 2020.1932-6203http://hdl.handle.net/11449/198507Background One 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. Methods We randomly generated unrelated data to estimate eGFR by common equations. Results Using 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. Conclusion While 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.engArtigo10.1371/journal.pone.02288422-s2.0-85079302639