Statistical analysis
Descriptive statistics summarized the proportion of PTB, overall and by
subtypes according to mode of conception. To study the effect of
confounding by indication of treatment and fertility status, inverse
probability of treatment weighting (IPTW) using propensity score was
applied.17,19 The weights were derived to obtain
estimates representing the average treatment effect for the treated
(ATT).17 The underlying propensity score model
included maternal age; obesity (i.e., pre-pregnancy BMI >
30 kg/m2, or, if BMI was missing from BORN, we used
the OHIP billing code for obesity [ICD-9 278); parity; smoking;
pre-pregnancy and gestational diabetes mellitus; chronic hypertension;
immigration; rurality; and income quintile; along with the statistical
interaction of parity with age, smoking with obesity, history of preterm
birth with age, history of preterm birth with diabetes, and income
quintile with age (Table S1). The balance between treatment population
was evaluated by standardised differences of baseline covariates, using
a threshold of > 0.1 signifying important
differences.20
The relation between mode of conception and PTB was quantified by
absolute rates and risk ratios (RR), derived using modified Poisson
regression with a robust error variance, which also accounts for
correlated errors among potentially more than one birth occurring in the
same woman,21 and adjusted for baselines
characteristics selected through ATT weights.17
This study was reviewed for ethical compliance by the Queen’s University
Health Sciences & Affiliated Teaching Hospitals Research Ethics Board
and received initial clearance on October 29, 2019 (Reference number is
6028050). Data were analyzed using SAS software version 9.1 (SAS
Institute, Cary, NC, 2010).