Prenatal paracetamol exposure and wheezing in infancy: a targeted maximum likelihood estimation application

Introduction Targeted maximum likelihood estimation (TMLE) is a semiparametric doubly-robust estimator that integrates the SuperLearner in the estimation process, an ensemble method that allows us to model the exposure-outcome relationship combining multiple parametric and non-parametric methods. Aim We applied TMLE to assess the effect of maternal paracetamol use during the first trimester of pregnancy on child wheezing during the first 18 months of life, using data of the Italian NINFEA birth cohort. Methods We included three progressively larger sets of covariates for confounding adjustment. Set 1 included baseline socioeconomic and maternal characteristics, conditions and disorders. Set 2 additionally included maternal respiratory infections in the first pregnancy trimester. Set 3 added prepregnancy maternal mental health disorders. The effect was estimated with three TMLE implementations, differing in the methods used to model the exposure-outcome relationship: (1) parametric; (2) SuperLearner with parametric and semiparametric approaches and (3) SuperLearner with parametric, semiparametric and non-parametric approaches, and with hyperparameters tuning. We compared TMLE with multivariable regression, propensity score regression adjustment and inverse probability weighting. Results All methods provided similar results, suggesting a weak positive association that attenuated toward the null as progressively more covariates were adjusted for, from set 1 (TMLE 3: risk ratio, RR 1.15 (95% CI 1.03 to 1.29)) to set 3 (TMLE 3: RR 1.10 (95% CI 0.97 to 1.26), N=4099). Conclusions Such an association could be interpreted as a small positive effect or incomplete control for residual or unmeasured confounding, and its consistency across methods suggests it is unlikely to be driven by model misspecification.