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In each model, the intervention effect will be estimated as the difference between the postintervention and preintervention levels of the outcome after adjusting for time.
A complier average causal treatment effect will be estimated using instrumental variable methods to assess efficacy if there is appreciable lack of compliance.
To account for the cluster randomisation among parent neonate dyads with multiple gestations, the SE for the treatment effect will be estimated via a bootstrap where dyads are resampled to preserve the correlation structure.
Quality adjusted life years (QALYs) effect will be estimated using the SF-6D algorithm for KDQOL data [ 22] and an Incremental Cost Effective Ratio ICERR) will be calculated [ 23].
In the first case (D → E), we will mistakenly estimate the effect of D on E instead of the effect of E on D. In the second case (E 0 → D 1 → E 2 → D 3…), neither effect will be estimated without bias.
The treatment effect will be estimated on an intention-to-treat basis including all patients with post-baseline data (carrying forward day 5 measurements if day 12 is missing), in an analysis of covariance including treatment and average baseline score as fixed factors and center as a random factor.
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In the proposed SEM, two types of effects will be estimated: direct and indirect.
Clinical relevance of the effects will be estimated by Cohen's effect sizes for statistically significant group differences (p < 0.05).
These incremental treatment effects will be estimated using multivariate ordinary least squares regressions, adjusting for baseline differences among treatment groups.
All effects will be estimated with a 95% confidence intervals and p-values from the corresponding hypothesis tests.
Eighteen months after randomization, intervention effects will be estimated and tested, adjusting for prerandomization levels, BMI, and gender.
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