Analysis of the primary hypothesis and outcome
REGRESSION, SENSITIVITY
We will determine the outcome of the primary hypothesis on an intention to treat basis. We will assign infants who exit the intervention at 35 days of methadone/morphine treatment either as 35 days of opioid treatment or we will treat them as a censored value at 35 days. We will determine intervention differences of two means by analyzing the average number of days of opioid treatment from the first weaning dose to cessation of opioid treatment. We will analyze the data using regression models that will include the intervention as a fixed effect and will include maternal treatment and stabilization dose as covariates. We will include site (hospital) in the model as a random effect. The primary test of interest will be the F-test of the intervention effect, and we will report the intervention difference along with 95% confidence interval (CI).Because of possible censoring and removal of participants due to intervention failure, we will conduct sensitivity analysis of the primary outcome using several methods. The first sensitivity analysis will replicate the analysis, described above, and include other covariates in the model that were significantly different across the two weaning intervention groups. Sensitivity analyses will also be conducted to evaluate the effects of infants who were and were not part of the Eat-Sleep Console trial as a covariate or effect modifier. Another possible sensitivity analysis will include non-parametric and/or survival regression (e.g., negative binomial, median regression, or survival analyses), as there is potential for skewness and censoring in the primary outcome.Finally, sensitivity analysis will include fitting a competing risks model to the data where the possible competing risk states are weaned, parental withdrawal, physician withdrawal, and treatment failure (unable to wean by 35 days of methadone/morphine treatment). We will fit Cox proportional hazards models to the data to estimate the intervention effect and other covariate effects on “cause-specific hazards.” The analysis of the “cause-specific hazards” will allow for additional inquiry into the intervention effect on the primary outcome while accounting for competing safety and withdrawal risks.Descriptive statistics (means, medians, SD, percentiles) for number of days of opioid treatment from the first weaning dose to cessation of opioid treatment will be generated and summarized in a table by treatment group.