Search

Classical epidemiology has focused on the control of confounding, but it is only recently that epidemiologists have started to focus on the bias produced by colliders. A collider for a certain pair of variables (e.g. an outcome Y and an exposure A) is a third variable (C) that is caused by both. In a directed acyclic graph (DAG), a collider is the variable in the middle of an inverted fork (i.e. the variable C in A → C ← Y). Controlling for, or conditioning an analysis on a collider (i.e. through stratification or regression) can introduce a spurious association between its causes. This potentially explains many paradoxical findings in the medical literature, where established risk factors for a particular outcome appear protective. We use an example from non-communicable disease epidemiology to contextualize and explain the effect of conditioning on a collider. We generate a dataset with 1000 observations, and run Monte-Carlo simulations to estimate the effect of 24-h dietary sodium intake on systolic blood pressure, controlling for age, which acts as a confounder, and 24-h urinary protein excretion, which acts as a collider. We illustrate how adding a collider to a regression model introduces bias. Thus, to prevent paradoxical associations, epidemiologists estimating causal effects should be wary of conditioning on colliders. We provide R code in easy-to-read boxes throughout the manuscript, and a GitHub repository [https://github.com/migariane/ColliderApp] for the reader to reproduce our example. We also provide an educational web application allowing real-time interaction to visualize the paradoxical effect of conditioning on a collider [http://watzilei.com/shiny/collider/]. [ABSTRACT FROM AUTHOR]

Background: There is limited knowledge about the optimal timing of antiretroviral treatment initiation in older children and adolescents.Methods: A total of 20 576 antiretroviral treatment (ART)-naïve patients, aged 1-16 years at enrolment, from 19 cohorts in Europe, Southern Africa and West Africa, were included. We compared mortality and growth outcomes for different ART initiation criteria, aligned with previous and recent World Health Organization criteria, for 5 years of follow-up, adjusting for all measured baseline and time-dependent confounders using the g-formula.Results: Median (1st;3rd percentile) CD4 count at baseline was 676 cells/mm 3 (394; 1037) (children aged ≥ 1 and < 5 years), 373 (172; 630) (≥ 5 and < 10 years) and 238 (88; 425) (≥ 10 and < 16 years). There was a general trend towards lower mortality and better growth with earlier treatment initiation. In children < 10 years old at enrolment, by 5 years of follow-up there was lower mortality and a higher mean height-for-age z-score with immediate ART initiation versus delaying until CD4 count < 350 cells/mm 3 (or CD4% < 15% or weight-for-age z-score < -2) with absolute differences in mortality and height-for-age z-score of 0.3% (95% confidence interval: 0.1%; 0.6%) and -0.08 (-0.09; -0.06) (≥ 1 and < 5 years), and 0.3% (0.04%; 0.5%) and -0.07 (-0.08; -0.05) (≥ 5 and < 10 years). In those aged > 10 years at enrolment we did not find any difference in mortality or growth with immediate ART initiation, with estimated differences of -0.1% (-0.2%; 0.6%) and -0.03 (-0.05; 0.00), respectively. Growth differences in children aged < 10 years persisted for treatment thresholds using higher CD4 values. Regular follow-up led to better height and mortality outcomes.Conclusions: Immediate ART is associated with lower mortality and better growth for up to 5 years in children < 10 years old. Our results on adolescents were inconclusive. [ABSTRACT FROM AUTHOR]