Analyses of secondary outcomes
death, HC, seizures, excessive stool output, respiratory disturbances
ADVERSE EVENTS, SENSITIVITY, SECONDARY, REGRESSION, EVENTS
We will use the same approach described in the primary outcome analysis to compare the number of days of opioid treatment with only infants receiving morphine as the primary pharmacological treatment for NOWS (Secondary Outcome 1). We will use this same approach to compare the number of days of opioid treatment using only infants receiving methadone as the primary pharmacological treatment for NOWS (Secondary Outcome 2).We will use adjusted logistic regression models to provide an odds ratio (OR) and 95% confidence intervals (CI) for binary secondary outcomes measured only once. These outcomes will include:The proportion of infants by intervention arms that escalate or resume opioid medication during weaning,For the proportion of infants by intervention arms with an atypical neurobehavioral profile,The proportion of infants who receive an escalation of a second-line or third-line drug to treat NOWS signs from the first weaning dose to cessation of opioid.We will adjust these models for the stratification variable (hospital), covariates of maternal treatment and stabilization dose, and baseline variables that may differ between groups by chance. We define the covariates that we may adjust for earlier in this section in the paragraph on other collected data.We will analyze the secondary outcome of the total opioid exposure from the first weaning dose to cessation of opioid in a similar manner as the primary outcome. We will use a regression model that will include a fixed treatment effect (intervention arms) and adjustment for the stratifying variable, covariates of maternal treatment and stabilization dose, and baseline variables that may differ between groups by chance as fixed effects. We will include hospital (site) as a random effect. The primary test of interest will be the F-test of the intervention arm effect, and we will report the treatment difference along with 95% CI.We will measure the secondary outcome of maternal well-being and maternal-infant attachment by using PROMIS Measures and MPAQ total scores. We will analyze these in a similar manner as the primary outcome. We will use a regression model to analyze PROMIS Measures and MPAQ total scores. This model will include a fixed treatment effect (intervention arms), an adjustment for the stratifying variable, and fixed effects for the covariates of maternal treatment and stabilization dose. The PROMIS Measures outcomes are measured at 1-month after discharge and 24-months of age. The models for PROMIS Measures outcomes will include the 1-month after discharge PROMIS Measures outcome as a covariate. The F-test of the intervention arm effect will be the primary test of interest. We will report the intervention arm difference along with 95% CI. We will conduct analyses among the entire group and among those where the biologic mother is the primary caretaker.We will measure the secondary outcome of infant neurobehavioral functioning using BITSEA scores. The BITSEA consists of two multi-item scales, a Problem scale (31 items) and a Competence scale (11 items). A high score on the Problem scale or a low score on the Competence scale is less favorable. We will analyze these in a similar manner as the primary outcome. We will use regression models to analyze the BITSEA total and subscale scores. This model will include a fixed treatment effect (intervention arms), an adjustment for the stratifying variable, and fixed effects for the covariates of maternal treatment and stabilization dose. The F-test of the intervention arm effect will be the primary test of interest. We will report the intervention arm difference along with 95% CI. We will conduct analyses among the entire group and among those where the biologic mother is the primary caretaker.We will calculate binomial proportion and their corresponding 95% CI by intervention arm for each of the following safety adverse events: seizures (clinical or EEG), excessive stool output, respiratory disturbances, and feeding tolerance. We will use chi-square tests to compare the proportion of seizures (clinical or EEG), excessive stool output, respiratory disturbances, and feeding tolerances between intervention arms. In addition to unadjusted analyses, we will compare AEs across the weaning interventions by using adjusted logistic regression models that adjust for the stratification variable (hospital), covariates of maternal treatment and stabilization dose, and possibly any baseline variables that were significantly different between weaning interventions. We define covariates that we may adjust for earlier in this section within the paragraph on other collected data.We will analyze the length of hospital stay in a similar manner as the primary outcome. We will use a simple, unadjusted regression model to analyze the total length of stay and will include a fixed treatment effect (intervention arms). A second regression model will include a fixed treatment effect and will include adjustment for the stratifying variable and covariates of maternal treatment and stabilization dose. The F-test of the treatment effect will be the primary test of interest. We will report the treatment difference along with 95% CI. Sensitivity analysis will involve time-to-event analyses using survival models to account for skewness and possible censoring of each outcome.We will analyze the caregiver questionnaire outcomes and the death outcome using a longitudinal generalized linear mixed-effects model (GLMM) or generalized estimating equations (GEE) model appropriate for the outcome type since the data will be collected at multiple time points after discharge. Count data that tend to have more than 0 or 1 events counted will be analyzed using a Poisson model while binary or count data that rarely goes beyond 1 occurrence will be analyzed using a Logistic model. In case of count data that rarely goes beyond 1 occurrence, this data will be transformed to binary data (occurrence/no occurrence). We will present mean outcome ratios for count data (from Poisson models) and odds ratios for binary data (from logistic models) with respect to the intervention effect as well as 95% CI of the intervention effect. All analyses will be adjusted for repeated measures over time, so that patterns of change for these outcomes over time can be assessed by treatment group.In addition to time of discharge and 24 months of age, anthropometric outcomes will be measured at birth. We will calculate anthropometric We will provide the mean and SD of infants’ weights (We will examine the impact of the treatment arm on length, head circumference (HC), and infant weight for length (To compare Bayley-4 scores between intervention arms, we will perform a linear mixed-effects model with a fixed effect for the intervention group and a random effect for study site. We will report point estimates for the group mean difference along with a 95% CI, and the team will repeat this analytical approach for each of the Bayley-4 domains.Descriptive statistics (means, medians, SD, percentiles) for continuous secondary outcomes and frequency-based statistics (N and percentages) for binary secondary outcomes will be generated and summarized in a tabular form by treatment group.