Propensity score approaches in quantifying effects of treatment from observational data

Introduction@#Despite the growing popularity of utilizing observational studies for determining associations with public health implications, there is limited literature using them for examining and quantifying the effects of exposures or treatments: The study compared traditional regression with scoring approaches in estimating treatment effects considering the noted limitations in the dataset. @*Methods@#We conducted a secondary analysis of previously collected retrospective cohort data derived from maternal-neonatal dyads delivered prematurely in a tertiary hospital. Propensity scores (PS) were estimated using logistic and boosting regression. These scores were implemented into matching, stratification, and weighting models. The estimated measures of effect from traditional regression and PS-adjusted models were compared using certain metrics (i.e., the width of CI, SE, AIC, BIC). Sensitivity analysis was also performed. @*Results@#We included data from 562 patients (123 untreated and 439 treated). Both the estimated scores demonstrated satisfactory fit and reduction in the standardized differences between the groups. However, the logit-estimated scores had better prediction (AUC: 0.71 vs 0.66) and forecasting properties (Brier: 0.15 vs 0.17) than the boosting-estimated scores. All generated statistical models demonstrated a reduction in the occurrence of respiratory morbidity among preterm neonates exposed to a single-dose antenatal corticosteroid (ACS) (ORs ranged from 0.37 to 0.59). The estimated average treatment effects (ATE) and effect among those treated (ATET) from various models suggested a small benefit attributed to the single-dose ACS (ATEs range from -0.09 to -0.41; ATETs range from -0.07 to -0.17). @*Conclusion@#PS estimated using logistic regression performed better than those estimated using machine learning strategies. The matching model using the said scores demonstrated better fit and parsimony over conventional and propensity-adjusted models. Future studies are recommended to improve the application of these analytic techniques in real-world data.

Average treatment effects of a single-dose antenatal corticosteroid on the respiratory morbidity of Filipino preterm neonates

Introduction@#Prematurity-related respiratory disorders are an important public health concern that should be treated efficiently and effectively. Antenatal corticosteroid (ACS) therapy has been recommended to hasten fetal lung maturation in pregnancies at risk but has not been delivered adequately in low to middle-income countries. This study aimed to estimate the treatment effects associated with the use of a single-dose antenatal corticosteroid on the incidence of respiratory-associated morbidity among prematurely delivered neonates. @*Methods@#This was a retrospective cohort study of neonates delivered at 24 to 33 weeks gestation at a tertiary hospital comparing outcomes in those given single-dose ACS with those given no ACS. Association was estimated using logistic and propensity score (PS) analyses, as well as average treatment effect (ATE) and among those treated (ATET). @*Results@#Most neonates (78.11%) received a single dose before delivery (single-dose ACS group) and only a few (21.89%) did not receive any dose (no ACS dose group). The odds ratio of respiratory morbidity in the single-dose ACS group was 0.44 (0.23-0.84) from an adjusted logistic regression model and 0.33 (0.17-0.80) from the PS matching model. The latter model was used to estimate that the average treatment effect from a single-ACS dose on the entire sample was -0.09 (-0.03 to -0.15), while its effect among the actual recipients was -0.08 (-0.02 to -0.15). @*Conclusion@#There is a small benefit attributed to the single-dose ACS, reinforcing the need for dose administration and completion. Future studies are recommended to clarify the estimated association and improve on the methodological constraints encountered.