Statistical methods.
Weight gain
REGRESSION
Weight gain in grams per day was calculated by calculating the difference in 6-month weight from baseline weight and dividing it by the number of days from enrollment to the 6-month study visit. Similarly, length change in millimeters per day was calculated as the difference in length at the two visits, divided by the number of days between visits. Unadjusted linear regression models were used to estimate the difference in grams per day and millimeters per day in neonates receiving azithromycin compared with placebo. Linear regression models adjusted for each baseline measure were used to estimate the difference in weight, length, WAZ, LAZ, WLZ, and MUAC at 6 months of age in infants randomized to azithromycin compared with placebo. Prespecified subgroup analyses for the primary outcome included age of enrollment by week (2, 3, or 4 weeks of age), sex (male or female), season of enrollment (rainy, defined as June through October, or dry), and urbanicity (urban, periurban, or rural). Subgroup analyses were similar to main analyses but included an interaction term for the subgroup by treatment arm for estimation of interaction on the additive scale and the main effect of the subgroup term. Models evaluating the effect of azithromycin on anthropometric endpoints were not adjusted for any covariates with the exception of the baseline measure for each outcome to improve statistical power. Because the intervention (azithromycin or placebo) was randomized, by definition there is no confounding (defined as a common cause of exposure and outcome), and no adjustment for confounders is necessary. Our prespecified analysis plan did not include adjustment for any covariates other than baseline measures, and all models followed the prespecified statistical analysis plan. All analyses were two-sided, and an alpha of < 0.05 was considered statistically significant. All analyses were conducted in R.