ReviewAmerican journal of hematology2023
Utilization of real-world data in assessing treatment effectiveness for diffuse large B-cell lymphoma.
Review in American journal of hematology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Efficacy of chimeric antigen receptor T cell therapy and autologous stem cell transplant in relapsed or refractory diffuse large B-cell lymphoma: A systematic review.Frontiers in immunology · 2022Pooled it
- Unlocking the Secrets of Regulated Cell Death in Large B-Cell Lymphoma Beyond Apoptosis: Signaling Pathways and Therapeutic Options.International journal of molecular sciences · 2026Review
- Comparison of Vital Status, Cause of Death, and Follow-Up after Hematopoietic Cell Transplantation in Linked Center for International Blood and Marrow Transplant Research and California Cancer Registry Data, 1991 to 2018.Transplantation and cellular therapy · 2024Article
- Challenges for quality and utilization of real-world data for diffuse large B-cell lymphoma in REALYSA, a LYSA cohort.Blood advances · 2024Article
- Utilization of real-world data in assessing treatment effectiveness for diffuse large B-cell lymphoma.American journal of hematology · 2023Review
- Autologous stem cell transplantation meets CAR-T therapy: A synergistic strategy for B-cell lymphoma.Cell transplantationReview
- A Cost-Effectiveness Analysis of Diffuse Large B-Cell Lymphoma Treatment Pathways in the United States.MDM policy & practiceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Direct comparisons of the effectiveness of the numerous novel therapies in the diffuse large B-cell lymphoma (DLBCL) treatment landscape in a range of head-to-head randomized phase 3 trials would be time-consuming and costly. Comparative effectiveness studies using real-world data (RWD) represent a complementary approach. Recently, several studies of relapsed/refractory (R/R) DLBCL have used RWD to create observational cohorts to compare patient outcomes with cohorts derived from single-arm phase 2 trials. Using propensity score methods to balance clinically and prognostically relevant baseline covariates, closely matched patient-level cohorts can be generated. By incorporating appropriate measures to assess covariate balance and address potential bias in comparative effectiveness study designs, robust comparative analyses can be performed. Results from such studies have been used to supplement regulatory approval of therapies assessed in single-arm trials. While RWD studies have a greater susceptibility to bias compared to randomized controlled trials, well-designed and appropriately analyzed studies can provide complementary real-world evidence on treatment effectiveness.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.