Methods in analysis to handle protocol non-adherence and any statistical methods to handle missing data {20c}
REGRESSION, DISEASE
We will use a PP approach for handling non-adherence and examine the robustness of the ITT analysis results. In the PP analysis, we will include patients based on their adherence to the protocol considered as complete cases (e.g., patients with more than 80% show up in the intervention arm and complete follow-up in the comparison arm) and to analyze the specified outcomes on between-group differences from baseline to week 12 for this population. As specified earlier, we apply similar mixed model analyses in the PP approach as in the ITT. In supplement to the PP analyses and the possible non-adherence, we plan to use causal inference methods that include emulating data, for example, effects of different types of non-adherence or effects of concomitant treatment utilizing marginal structured models and inverted probability weighting to estimate the average treatment effect under these explorative additional questions [In the case of missing data in the ITT analysis, we will use multiple imputations with 10 imputations per missing value if the data is assumed to be missing at random or not missing at random. The multiple imputations will be based on regression models including predictive covariates and/or outcomes depending on which data are missing. We plan to use a linear model for continuous outcomes including covariates such as age, sex, type of disease, and disease activity. If data is assumed not to be missing at random, we will attempt to understand why and report the differences in our results by the complete-case approach and when imputation analyses are applied. To deal with any covariates or outcomes that are not normally distributed we will use log or other appropriate transformations [