Stata statistical software release 15

Prior to fitting regression models, we log₂-transformed ∑DEHP given its non-normality. We modeled the relation of a doubling of ∑DEHP with odds of preterm birth via logistic regression models, estimated using a robust Huber-White sandwich estimation of variance. We included as covariates maternal age (years), pre-pregnancy BMI, maternal education level (highest achieved: high school or less, any college/technical school, any graduate work), and maternal race (White, Black, Other/multiple race). We also included study site to account for systematic differences across sites and inherent correlation of observations within sites. For models applying the regression-based or covariate-adjusted standardization method, we additionally included z-scores of the measure(s) of dilution as an additional covariate. However, as a sensitivity analysis, we fitted models where we applied the covariate-adjusted standardization method but excluded the z-scores of the measure(s) of dilution, as a previously described variation of this general approach.^{17 (link)}In regression analyses, we addressed missing covariate data (7% of sample) using multiple imputation by chained equations (50 imputed data sets). We performed all analyses using Stata v15.1 (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC).

All survey responses were collected anonymously. Data was stored on Google Docs, which is stored securely, with data encrypted both in-transit and at-rest.²² We calculated a mean score for each authenticity question. A Shapiro-Wilk test demonstrated that our data was not normally distributed (P < .05). Thus, we performed a Wilcoxon Signed Rank Test to compare the mean scores of authenticity for actively and passively intense emotional expressions and clinical experiences for each of 2 sessions. A P-value of <.05 was considered statistically significant (2 sided). Statistical analyses were performed using StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC. Two study team members who are fluent in Japanese (EO, a female family medicine and palliative care physician and KO, a male emergency physician with experience in qualitative data analysis) independently reviewed free text responses which were written in Japanese to develop preliminary code list. Then, EO and KO reviewed the comments line by line to determine which codes fit the themes suggested by the data independently. Themes identified were reconciled and discussed until mutual agreements have been reached.