Data quality control in longitudinal epidemiologic studies: conditional studentized residuals from linear mixed effects models for outlier detection in the setting of pediatric chronic kidney disease

Abstract

PurposeQuality control in longitudinal cohort studies is critical for valid epidemiologic inference. Conditional studentized residuals (CSRs) derived from linear mixed effects models offer efficient individual-specific quality control. We present the utility of CSRs for outlier detection in an applied example using data from the Chronic Kidney Disease in Children cohort. MethodsLongitudinal linear mixed effects models with glomerular filtration rate (GFR) as the outcome were fit for observations prior to kidney replacement therapy, stratified by nonglomerular or glomerular diagnosis, and for a subset after receiving a kidney transplant. For each model, CSRs were calculated and values ≥±5 were considered potential outliers for further investigation. ResultsA total of 1096 participants contributed 6881 annual measures of GFR across the two diagnostic groups and after transplant. In all models, the fixed effects captured progressive GFR decline. CSRs provided measures of individual-level deviations from the modeled trajectories (random + fixed effects) and were easily visualized in longitudinal plots. A total of 38 potential outliers from 32 participants were detected and further investigated for quality control. ConclusionsThis example demonstrated how longitudinal models can provide CSRs to detect individual-specific outliers. CSRs should be considered as part of quality control for longitudinal epidemiologic studies.