Analysis
All analyses were conducted using STATA 17 (College Station, TX). We assessed whether the proportions of participants who reported miscarriages or stillbirths were similar across study arms using Chi-squared or Fisher’s Exact tests. Further, we compared baseline participant characteristics between those missing and not missing birth outcome data using Chi-squared or Fisher’s Exact tests for dichotomous variables and Student’s t-test or the Wilcoxon-Mann-Whitney tests for continuous variables.
We provided descriptive statistics of the study sample by intervention and standard-of-care arm assignment. We assessed the balance between the arms using Chi-squared or Student’s t-tests. To address potential clustering and unmeasured confounding, we assessed results from three statistical models. First, we utilized a multivariable logistic regression model. Next, we estimated a fixed effects model in an attempt to purge some effects of unobserved clinic-level characteristics that may influence birth outcomes.(18) We also estimated a random effects model, with the clinic specified as a random effect. As the intraclass correlation coefficient was 0.0017, suggesting that only 0.17% of the variance in the outcome was due to variations across clinics, we decided not to estimate a generalized estimating equation. For each model, covariates of interest (described above) were entered into a full model and then excluded one at a time, starting with the variable with the largest p-value until all remaining independent variables had p-values <0.20. Then, covariates were readded one at a time in the order they were dropped and retained if they had a p-value of <0.20 or the odds ratio (OR) of the primary exposure was changed by 15%. We also assessed the potential interaction between clinic and intervention assignment. Models were run for the primary outcome (preterm or low birth weight), as well as preterm birth only (includes low birthweight if preterm) and low birth weight only (includes preterm if low birth weight) individually. When the clinic site variable was included, we assessed the models with and without the clinic that did not crossover.
Further, we conducted post-estimation predictive margins analysis to estimate the predicted prevalence of the composite outcome of low birth weight or preterm birth in the intervention and standard of care arms as well as the adjusted risk ratios. In other words, following the logistic regression, all participants were set to both exposure values (standard-of-care and intervention) and the logistic regression coefficients were used to calculate the predicted prevalence for everyone at their observed confounder pattern and assigned exposure. Confidence intervals were calculated using the delta method.(19) Thereafter, the effect of intervention versus the standard-of-care was calculated as an adjusted risk ratio.
In another attempt to balance confounding, we conducted post hoc analysis that stratified the sample by nulliparous and multiparous women.