{"doi":"10.1002/cncr.33252","title":"Assessing clinical trial effects on outcomes among pediatric and adolescent and young adult (AYA) patients with cancer","abstract":"We read with great interest the study by Schapira et al,1 which compared overall survival and morbidity among pediatric and adolescent and young adult (AYA) patients with cancer who were treated in phase 3 clinical trials with those of matched patients receiving off-trial care. This thought-provoking study raises key considerations for ongoing efforts to improve access and equity in clinical trial enrollment, as well as for future research studies comparing the outcomes of patients enrolled in trials with those of patients receiving care in clinical practice. One finding of interest to our group was the continued underrepresentation of Black children in phase 3 trials. According to Table 1 in the study,1 49 of the patients who received off-trial care (21.5%) were Black, whereas only 42 of those treated on the clinical trial (9.7%) were.1 This is consistent with work demonstrating the underrepresentation of Black non-Hispanic children with lymphohematopoietic tumors in Children's Oncology Group clinical trials.2 Given the racial disparities in survival among pediatric patients with cancer,3 more work is needed to develop and implement programs, similar to recent efforts in the Chicago area,4 that address the system-level, individual-level, and interpersonal-level barriers5 to participation in pediatric oncology clinical trials among underrepresented patients. Furthermore, we noted age differences between children and AYA patients treated on and off trial. For example, among patients with acute lymphocytic leukemia, there was an underrepresentation of younger (<12 months) patients being treated on clinical trials (6 patients; 2.0%) compared with off the trials (10 patients; 6.4%). Among patients with acute myeloid leukemia, there was an underrepresentation of patients aged 1 to 2 years treated on clinical trials (7 patients; 14.3%) compared with those receiving off-trial care (9 patients; 33.3%). Given these observed differences between patients treated on and off clinical trials, we commend Schapira et al1 for using a matching approach to reduce baseline imbalances in risk factors related to morbidity or mortality and factors potentially related to trial enrollment when comparing outcomes in these populations. We would like to highlight two important points for future research studies seeking to compare the outcomes of patients enrolled in trials with those of patients treated in clinical practice: 1) the potential for confounding due to the experimental treatment effects; and 2) the advantages of model-based standardization approaches. First, in the study by Schapira et al, treatment assignment among trial participants was unavailable, and therefore matching based on treatment was not possible. Grouping all trial participants together, regardless of study arm, means that the effects of trial enrollment cannot be separated from the effects of the experimental treatment. This is problematic because the treatments offered on and off of clinical trials are heterogenous in terms of their efficacy and safety profiles. Although the current study and many others6 did not include treatment data, future studies should proactively collect or obtain treatment information for patients treated both on and off clinical trials because this can be used to more clearly isolate the effect of trial participation on outcomes. Second, we recommend the use of model-based standardization methods when seeking to compare outcomes of patients treated on and off of clinical trials. In a recent study, our group compared survival among patients treated on and off clinical trials with the same standard chemotherapy regimen for metastatic colorectal cancer (folinic acid [leucovorin], 5-fluorouracil, and oxaliplatin [FOLFOX] plus bevacizumab).7 Patient-level trial data were accessed through Project Data Sphere8 and individual-level off-trial patient data were obtained from the Surveillance, Epidemiology, and End Results–Medicare-linked database. We first comp","journal":"Cancer","year":2020,"id":131484,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9566,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":426824,"name":"Jeff Y. Yang","orcid":"0000-0002-0817-983X","position":1,"is_corresponding":false},{"id":585501,"name":"Sydney Thai","orcid":"0000-0001-5978-6579","position":2,"is_corresponding":false},{"id":513645,"name":"Michael Webster‐Clark","orcid":"0000-0002-8079-8504","position":3,"is_corresponding":false},{"id":387620,"name":"Jennifer L. Lund","orcid":"0000-0002-1108-0689","position":4,"is_corresponding":false},{"id":427884,"name":"Shahar Shmuel","orcid":"0000-0003-1726-1875","position":0,"is_corresponding":true}],"reference_count":10,"raw_metadata":null,"created_at":"2026-07-18T23:16:03.875886Z","pmid":"33119144","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}