A comparative-effectiveness analysis applying a 3 way propensity matching to real-world data from MSBase Registry in preparation for a cost effectiveness model: Patients switching within firstline agents or to Natalizumab or Fingolimod in active RRMS

Author(s):  
Tim Spelman
2021 ◽  
Vol 12 ◽  
Author(s):  
Z. Kevin Lu ◽  
Xiaomo Xiong ◽  
Taiying Lee ◽  
Jun Wu ◽  
Jing Yuan ◽  
...  

Background: Big data and real-world data (RWD) have been increasingly used to measure the effectiveness and costs in cost-effectiveness analysis (CEA). However, the characteristics and methodologies of CEA based on big data and RWD remain unknown. The objectives of this study were to review the characteristics and methodologies of the CEA studies based on big data and RWD and to compare the characteristics and methodologies between the CEA studies with or without decision-analytic models. Methods: The literature search was conducted in Medline (Pubmed), Embase, Web of Science, and Cochrane Library (as of June 2020). Full CEA studies with an incremental analysis that used big data and RWD for both effectiveness and costs written in English were included. There were no restrictions regarding publication date. Results: 70 studies on CEA using RWD (37 with decision-analytic models and 33 without) were included. The majority of the studies were published between 2011 and 2020, and the number of CEA based on RWD has been increasing over the years. Few CEA studies used big data. Pharmacological interventions were the most frequently studied intervention, and they were more frequently evaluated by the studies without decision-analytic models, while those with the model focused on treatment regimen. Compared to CEA studies using decision-analytic models, both effectiveness and costs of those using the model were more likely to be obtained from literature review. All the studies using decision-analytic models included sensitivity analyses, while four studies no using the model neither used sensitivity analysis nor controlled for confounders. Conclusion: The review shows that RWD has been increasingly applied in conducting the cost-effectiveness analysis. However, few CEA studies are based on big data. In future CEA studies using big data and RWD, it is encouraged to control confounders and to discount in long-term research when decision-analytic models are not used.


Author(s):  
Robert C Doebele ◽  
Laura Perez ◽  
Huong Trinh ◽  
Michael Martinec ◽  
Reynaldo Martina ◽  
...  

Aim: Generating direct comparative evidence in prospective randomized trials is difficult for rare diseases. Real-world cohorts may supplement control populations. Methods: Entrectinib-treated adults with advanced ROS1 fusion-positive NSCLC (n = 94) from Phase I/II trials (ALKA-372-001 [EudraCT2012-00148-88], STARTRK-1 [NCT02097810], and STARTRK-2 [NCT02568267]) were compared with a real-world crizotinib-treated cohort (n = 65). Primary end point, time-to-treatment discontinuation (TTD); secondary end points, PFS and OS. Results: Median (95% CI) weighted TTD: 12.9 (9.9–17.4) months for entrectinib; 8.8 (6.2–9.9) months for crizotinib (weighted hazard ratio, 0.72 [0.51–1.02]). Median OS with entrectinib was not reached, weighted median OS with crizotinib was 18.5 (15.1–19.9) months. Conclusion: Entrectinib administered in clinical trials may be associated with longer TTD than a real-world crizotinib population.


Sign in / Sign up

Export Citation Format

Share Document