Feasibility assessment of indirect treatment comparison between off-label rituximab and novel treatments in patients with warm autoimmune hemolytic anemia
Bibliographic record
Abstract
Abstract Introduction: Indirect treatment comparisons (ITCs) provide valuable evidence on comparative efficacy where head-to-head clinical trials do not exist; however, differences in patient and study characteristics may introduce bias [Jansen JP et al., 2011]. Therefore, it is essential to do a systematic and thorough assessment to determine the suitability of different ITC methods. In warm autoimmune hemolytic anemia (wAIHA), rituximab has been widely used off-label, despite lacking regulatory approval for this indication. With several novel targeted therapies now emerging through registrational trials, there will be a critical need for ITCs to contextualize their efficacy relative to the off-label use of rituximab. We provide an illustrative case study in wAIHA where we assess the feasibility of conducting ITCs between studies investigating off-label rituximab and novel treatments. Methods: A systematic literature review was conducted using multiple databases with the Ovid® platform (Embase, Ovid MEDLINE®, and the Cochrane Central Register of Controlled Trials) from January 2013 to December 2024 to retrieve all published interventional studies in wAIHA. We assessed the feasibility of conducting ITCs using standard methods (Bucher ITC or network meta-analysis [NMA]) as well as population-adjusted methods (matching adjusted indirect comparison [MAIC], simulated treatment comparison [STC]) using data from published rituximab trials and existing real-world evidence studies, as well as information available for investigated novel treatments. Results: The electronic database search identified 3,168 records (excluding duplicates) that were screened at the title and abstract stage and 390 records were obtained and assessed for eligibility in full-text review of which 10 were included. In addition, conference abstracts, key regulatory and HTA agencies, and bibliographies of relevant SLRs captured in the database search were hand searched for additional relevant studies. Out of 870 additional records retrieved, 863 were excluded and 7 were included. Thus, this review identified 17 records pertaining to 12 unique studies investigating therapies in patients with wAIHA. All study populations consisted of participants with wAIHA or reported a wAIHA subgroup, except two studies that reported a mixed population (without reporting on the wAIHA subgroup) where 95.5% and 88% of participants had wAIHA. Most trials were at least 24 weeks long, with three extending for longer than two years. Five clinical trials were randomized whereas the remaining seven were single-arm trials. Four trials had control arms, three of which included placebo, and one control arm was prednisolone. Among the clinical trials included, five studied rituximab, in combination with prednisone, prednisolone, ibrutinib, or bortezomib. Three trials studied fostamatinib, while other studied treatments included pegcetacoplan, sovleplenib, parsaclisib, and rilzabrutinib. There were large differences in study design, eligibility criteria, country, average age, timing of study, and sample sizes between rituximab and studies investigating novel treatments. There are also large differences in endpoint definitions and timepoint of endpoint evaluation between rituximab and other studies. Bucher ITCs and NMAs were not feasible due to the absence of common treatment arms and could not account for important cross-trial differences due to their reliance on summary-level data. Comparisons between rituximab and registrational trials using unanchored population-adjusted methods such as MAIC and STC are also limited due to lack of overlap in patient populations in terms of treatment line, and differences in background corticosteroid use, which are both critical prognostic variables. Conclusions: An ITC using published rituximab interventional studies was found to not be feasible as it is unlikely to yield credible results due to lack of a common comparator and differences in key prognostic variables including patient characteristics and background corticosteroid use. Future work should consider de novo sources of real-world evidence for rituximab that more closely align with registrational trial characteristics and endpoint definitions. However, aligning timing of endpoint measurements between registration trials and real-world data to match definitions remains challenging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".