医学
内科学
多发性骨髓瘤
肿瘤科
人口
无进展生存期
耐火材料(行星科学)
随机对照试验
总体生存率
天体生物学
环境卫生
物理
作者
Jianming He,Karen Chiang,Xiwu Lin,Winghan Jacqueline Kwong,John Maringwa,Lingfeng Yang,Sandhya Nair,Mahmoud Hashim,Mi Jun Keng,Imtiaz A. Samjoo
标识
DOI:10.57264/cer-2024-0180
摘要
Aim: We aimed to identify variables that affect the prognosis and treatment effect in multiple myeloma (MM). Materials & methods: Published literature of randomized controlled trials (RCTs) and population-adjusted indirect comparisons (PAICs) in newly diagnosed (ND) and relapsed/refractory (RR) MM populations reporting overall survival (OS) and progression-free survival (PFS) were identified. Possible treatment effect modifiers (TEMs) were evaluated based on the ratio between effect estimates of different strata within subgroups for OS and PFS among eligible RCTs. Potential prognostic factors (PFs) were identified using the lists of covariates adjusted for in PAICs meeting the eligibility criteria. Results: Sixty-five RCTs and 59 PAICs were included for synthesis. In ND-MM and RR-MM patients, age, sex, International Staging System stage, and cytogenetics were identified as potential TEMs for PFS based on data from published RCTs. Refractory disease, prior therapy exposure status and creatinine clearance were additional TEMs for PFS in RR-MM patients. Eastern Cooperative Oncology Group performance score and creatinine clearance were TEM candidates of PFS for ND-MM stem-cell transplant-ineligible patients. No consistent TEMs for OS were identified across all MM populations. Commonly adjusted variables for both OS and PFS in published PAICs of all populations aligned with potential TEMs of PFS identified in published RCTs. Additionally, subtype of MM, time since diagnosis and extramedullary disease or presence of plasmacytoma were common variables for adjustment in PAICs evaluating RR-MM. Frequency of each variable adjusted for differs by population and outcome. Only one PAIC reported TEMs separately from PFs. Conclusion: TEMs and PFs identified herein can help inform future clinical trial design and serve as a primer when conducting PAICs evaluating OS and PFS in ND/RR-MM.
科研通智能强力驱动
Strongly Powered by AbleSci AI