Data analysis
REGRESSION
The study variables were summarized using means and standard deviations or frequency distributions, as appropriate. Group comparisons of maternal demographic or personal factors and prenatal sleeping intentions were done using the two-sample t-test or chi-square test of association. For a few comparisons with small expected cell counts, Fisher’s exact test was used as an alternative to the chi-square test of association. To evaluate changes in safer sleeping practices over time and between group differences, this binary outcome that was differentiated between safer (i.e. scores of 2–3 on the sum of the indicators for the three items) and less safe (i.e. scores of 0–1) sleep intentions or practices was assessed for the main effects of time and group and their interaction using generalized estimating equation (GEE) modeling. This type of modeling is an extension of logistic regression for longitudinal designs. Covariates included in the GEE model were age, race/ethnicity, family income, marital status, parity, PHQ-2 indicator, and the indicator for maternal use of cigarettes, alcohol, or other drugs. Post-hoc analysis was conducted using pre-specified contrasts. We were interested in differences between the two groups at each assessment rather than differences within a given group over time. We 