Statistical Analysis
The exposure was AYA cancer, and the outcomes were preterm birth and
SMM. Descriptive characteristics were calculated using frequency and
percentages and compared by exposure group Student’s t-test or
Chi-square test of proportions, as appropriate. Distributions of
continuous variables were assessed for normality and reported as
mean±SD. Due to skewed distribution, the Maternal Comorbidity Index was
log-transformed for analysis. Analysis was stratified by singleton
versus multiple live birth, because of known effect modification by
pregnancy plurality.33
To compare differences in maternal characteristics between births for
exposed and unexposed females, we used log-binomial regression models to
estimate risk ratios (RR) and 95% confidence intervals (CI) for the
outcomes of interest with adjustment for potential confounders (Model
1). We tested for effect modification based on p-values from
cross-product terms in regression models. Maternal comorbidities were
hypothesized mediators, or causal intermediates, of the relationship
between AYA cancer and adverse perinatal outcomes (Model 2). Mediation
analysis was undertaken to obtain estimates accounting for the mediator
and to estimate the proportion mediated by comorbidities, accounting for
potential interactions and non-linear relationships.34
Mediation analysis via a counterfactual framework was conducted using
PROC CAUSALMED in SAS35, and GLM regression models
were analyzed in R. Subgroup analyses were performed: 1) excluding
gynecological cancers because of known associations between these and
preterm birth, and 2) restricting the study period from 2010 to 2019 to
considering temporal advances in cancer and perinatal care. All tests of
significance were two-tailed, and alpha was 0.05.