Limitations
DISEASE
As an analysis of observational data, the challenges of missing outcomes data and nonrandomized treatment assignment were possible limitations to this study that may impact interpretation of the generalizability of the results. Our analysis relied on some assumptions, and violation of these would limit the reliability of the results. We assumed the joint counts, CRP levels, and patient global scores were missing at random (ie, whether an outcome was missing was not related to its value after conditioning on the observed data). Under this assumption, the missing data on joint counts, CRP levels, and patient global scores could be accounted for in the imputation model by the characteristics of the patients, the treating rheumatologists, and the clinics, which were fully observed, and accounting for these variables could produce unbiased results in the analysis. Availability of nursing staff time and other characteristics of the clinics and individual rheumatologists are plausible variables to explain whether a rheumatologist in a busy clinic would be able to prioritize recording complete data on all outcomes during a consultation with a patient. Nevertheless, we acknowledge that there may have been bias that could not be addressed if there were further unmeasured sources of missing data that were not accounted for in the imputation model.The assumptions for inferring a causal treatment effect include that (1) the probability of initiating treatment with ADA or TOF may have depended on the measured baseline characteristics but did not depend on future disease activity at follow-up time points, (2) the probability of treatment assignment did not equal 0, and (3) a patient's observed DAS28-CRP at follow-up was the same as that patient's potential DAS28-CRP at follow-up after they followed the treatment that they were observed to be assigned to.