Causal Inference and Survey Data in Paediatric Epidemiology: Generalising Treatment Effects From Observational Data.

Abstract

Background: Survey data are essential in paediatric epidemiology, providing valuable insights into child health outcomes. The potential outcomes framework has advanced causal inference using observational data. However, traditional design-based adjustments, especially sample weights, are often overlooked. This omission limits the ability to generalise findings to the broader population.

Objective: This study demonstrates three approaches for estimating the population average treatment effect (PATE) in a practical example, examining the impact of household second-hand smoke (SHS) exposure on blood pressure in school-aged children.

Methods: Using data from the National Health and Nutrition Examination Survey (NHANES) 2017-2020, we assessed the effect of household SHS exposure, a non-randomised treatment, on blood pressure in school-aged children. We applied estimators based on Inverse Probability of Treatment Weighting (IPTW), G-computation, Targeted Maximum Likelihood Estimation (TMLE), and regression adjustment. Models without adjustments were run for comparison. We examined point estimates and the efficiency of the estimates obtained from these methods.

Results: The largest differences were observed between the unadjusted regression models and the fully adjusted methods (IPTW, G-computation, and TMLE), which account for both confounding and survey weights. While the inclusion of the sample weights leads to wider confidence intervals for all methods, G-computation and TMLE showed comparatively narrower confidence intervals. Confidence intervals for the models not adjusted for sample weights were likely underestimated.

Conclusions: This study highlights the important role of sample weights in causal inference. Generalisability of the average treatment effect as estimated on data sampled using common survey designs to a defined population requires the use of sample weights. The estimators described provide a framework for incorporating sample weights, and their use in health research is recommended.