Study Cohort and Design

Patients were drawn from the CorEvitas (formerly Corrona) registry and were adults aged > 18 years with a diagnosis of RA [16]. The registry database as of 1 January 2022 was used for analyses. As of that date, data on 57,543 patients with RA had been collected since inception, recruited from 211 private and academic practice sites across 43 states in the USA, with 911 participating rheumatologists. Data were collected from both patients and their treating rheumatologists using structured case report forms and include information on disease duration, prognosis, disease severity and activity, comorbidities, use of medications, and patient-reported outcome data.

All participating investigators were required to obtain full board approval for conducting research involving human subjects. Sponsor approval and continuing review were obtained through a central Institutional Review Board (IRB) (New England Independent Review Board [NEIRB] No. 120160610). For academic investigative sites that did not receive a waiver to use the central IRB, approval was obtained from the respective governing IRBs and documentation of approval was submitted to the Sponsor prior to initiating any study procedures. All registry subjects were required to provide written informed consent prior to participating. The study was conducted in accordance with the Helsinki Declaration of 1964 and its later amendments.

Two sets of patients were included for this analysis: switchers and controls (non-switchers)

Inclusion and Exclusion Criteria for the Group that Switched Biologics/tsDMARDs

Patients included as switchers had at least two visits prior to the switch while on drug (at least 1 visit after initiation and 1 visit prior to switching). The visit prior to the switch was between 2 and 12 months of the switch. The patient had to discontinue the original initiated drug no more than 6 months prior to the switch to a new drug. This interval of 6 months was chosen to ensure that discontinuation of the original drug was done in the context of a switch rather than an attempt to taper off medications or for some other reason associated with stopping the original drug. The visit prior to the switch must have been at least 3 months after the initiation. The patient needed to have a Clinical Disease Activity Index (CDAI) score for the two visits prior to the switch and a baseline CDAI score (CDAI score at time of initiation). In the scenario where an individual patient had ≥ 2 while in the CorEvitas registry, the first initiation/switch of a patient was used for the analysis.

Inclusion and Exclusion Criteria for Control Patient Visits (Who Did Not Switch)

Patients could be included as controls if they had at least two visits while on the initiated biologic/tsDMARD drug (one could be initiation visit; Fig. 1) and did NOT switch after those visits. We excluded patients used in the switch group from the control group. A control visit was matched to a switch visit by drug class, prior bDMARD/tsDMARD experience, and time from initiation (Fig. 1). The visit after the matched control visit could not be a discontinuation of the biologic/tsDMARD.

Fig. 1

Timeline of visits and measurements of disease activity for switchers and controls

Statistical Analysis

The pairs of switch and control visits were randomly divided into a training dataset (60%) and a test dataset (40%). The training dataset was used to determine a best prediction model and applied to the test dataset for estimation of the probability of switching.

The set of covariates described in the baseline table were considered, including patient demographics (sex, age, race, education, work status, insurance, smoking); disease status (comorbidities, disease duration); measures of disease (CDAI, joint counts, patient and provider global assessments, patient pain, Modified Health Assessment Questionnaire [mHAQ], AM [morning] stiffness, Routine Assessment of Patient Index Data (RAPID)); and measurements of chronological time (year of initiation). For disease measures, values at the visit prior to switch, the change in values at the visit prior to switch, and the change from baseline were all considered as potential predictors. For change measures, LOWESS curves illustrated separate slopes for changes below and above zero, so linear splines with a knot at zero were used for disease change covariates.

We used data that were available for all models since there was minimal missingness for covariates examined. The covariates selected for potential use were ones that had minimal missingness in the registry (all disease activity measures combined had 0.2% missing). The final model chosen in the training dataset had an n = 3006 (vs. 3016 total — 0.3% missing); the test set had 0.2% missing. In cases where there was more than 'minimal' missing—for example, RAPID (12% missing) or disabled indicator (1.8% missing)—we examined potential prediction with its addition. The plan, if it showed improvement, would have been to examine if it was the covariate (RAPID) or the subpopulation used.

Three methods were used to examine potential models and reduce overfitting: best subset regression, lasso [17], and ElasticNet [18]. Best subset regression selects using log likelihood; lasso adds a penalty on coefficients, forcing some to zero; ElasticNet uses a combination of ridge regression and lasso. The best models from these methods were examined for prediction (in the training dataset) of estimating the probability of switching and the area under receiver operating characteristic (ROC) curve (AUC). The best models from each method were examined and further simplified if the AUC was not reduced. A final model was chosen and that model applied to the test dataset once to provide estimates of prediction of switching. Dominance statistics were used to determine ranked importance of predictors [19, 20]. All analyses were carried out using Stata 17.0 (StataCorp, LLC, College Station, TX, USA). Specific Stata commands used for model selection are listed in Electronic Supplementary Material (ESM) Appendix 1, and a summary of the dominance analysis is given in ESM Appendix 2. A probability of switching calculator was created using the coefficients from the best model selected.

For a sensitivity analysis, the model coefficients were varied to the upper and lower 95% confidence limit estimates of each coefficient, and the probability of switching and AUC were estimated to examine the stability of the model.