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Table 4 shows the results of using the marginal structural model to simulate the intervention scenarios.
The HR for SSSI for anti-TNF adjusting for dropout using the marginal structural model was 0.9 (95% CI 0.5 to 1.4).
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We used the marginal structural model to derive adjusted, five year event probabilities for two hypothetical trajectories of the time dependent covariate: no treatment throughout follow-up, or constant treatment throughout follow-up.
This improvement was therefore not large enough to move any individuals from low to medium, or from medium to high, in the categorical mediator that was used in the marginal structural model.
Mrof is the AMLR alignment obtained using the marginal method.
The dataset 'main_w' contains the inverse probability of treatment weights and is the dataset used to run the marginal structural model analysis.
However, it has been shown that elaborate statistical methods, such as the marginal structural model using IPTW, provide a better method of control for confounding influences by improving the adjustment of differences between treatment groups [ 20].
Based on the marginal structural model fit using targeted maximum likelihood estimation, we estimated the probabilities of virologic failure at each set stratum of adherence, for duration of continuous suppression ranging from 1 to 12 months.
We used the R-package "IPW" 48 for the marginal structural model analysis.
The marginal structural model was fit using a targeted maximum likelihood estimator (TMLE) [27], in order to adjust for the potential confounders listed above.
The marginal structural model was estimated using multiple logistic regression analysis.
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