Comparative Effectiveness of Nonbiologic versus Biologic Disease-modifying Antirheumatic Drugs for Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the comparative effectiveness of nonbiologic disease-modifying antirheumatic drugs (DMARD) versus biologic DMARD (bDMARD) for treatment of rheumatoid arthritis (RA), using 2 common analytic approaches. METHODS: We analyzed change in Clinical Disease Activity Index (CDAI) scores in patients with RA enrolled in a US-based observational registry from 2001 to 2008 using multivariable (MV) regression and propensity score (PS) matching. Among patients who initiated treatment with a nonbiologic DMARD (n = 1729), we compared patients who switched to, or added, another nonbiologic (n = 182) or a bDMARD (n = 342) at 5, 9, and 24 months after treatment change. RESULTS: Both analytic approaches showed that patients switching to or adding another nonbiologic DMARD demonstrated improvement across 9 and 24 months (both p < 0.001). Both approaches also demonstrated greater improvement in CDAI among recipients of bDMARD relative to a second nonbiologic DMARD at 5 months (p < 0.02). The MV regression approach upheld these results at 9 and 24 months (p < 0.03). In contrast, the PS-matching approach did not show a sustained advantage with bDMARD at these later timepoints, possibly because of lower statistical power and/or lower baseline disease activity in the PS-matched cohort. CONCLUSION: Patients in both treatment groups generally experienced lower CDAI scores across time. Patients switching to bDMARD demonstrated greater improvement than patients switching to nonbiologic DMARD with both analytic approaches at 5 months. Relative advantages with bDMARD were observed at 9 and 24 months only with MV regression. These analyses provide a practical example of how findings in comparative effectiveness research can diverge with different methodological approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".