Multistep Analytical Procedure Developed to Estimate the Average Treatment Effect (ATE) of Tofacitinib (TOF) Compared With Adalimumab (ADA)
Random forest multiple imputation (RF-MI) was used to impute plausible values for missing data in the original electronic medical record (EMR) data set. Stable balancing weights (SBWs) were used to balance the baseline characteristics of the ADA and TOF treatment groups. Gray squares in the grid that represents EMR indicate complete data items and white squares, missing data items. In the complete data sets generated by RF-MI, colored squares represent the imputed values. The different colors used for the same data item in different data sets indicate that imputed values were slightly different in each data set. The covariate balance plots indicate that after SBWs were applied, the mean standardized difference in baseline characteristics between the treatment groups was close to 0 compared with before weighting. The ATE was then calculated separately in each imputed data set before pooling to generate a final estimate.In brief, multiple imputation by chained equations using the random forest algorithm under the missing-at-random assumption was used to impute missing data for the components of the DAS28-CRP at baseline and follow-up, generating 10 imputed data sets.All analyses were performed using R, version 4.0.2 (R Project for Statistical Computing). Differences in mean DAS28-CRP at follow-up were assessed using a 2-sided test and a significance level of 