EconPapers    
Economics at your fingertips  
 

Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments

Jen-Yu Amy Chang (), James B. Chilcott and Nicholas R. Latimer
Additional contact information
Jen-Yu Amy Chang: University of Sheffield
James B. Chilcott: University of Sheffield
Nicholas R. Latimer: University of Sheffield

PharmacoEconomics, 2024, vol. 42, issue 5, No 3, 487-506

Abstract: Abstract With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a ‘roadmap’ of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines—statistics, epidemiology, causal inference, operational research and computer science—has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectiveness and future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40273-024-01363-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:pharme:v:42:y:2024:i:5:d:10.1007_s40273-024-01363-1

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/40273

DOI: 10.1007/s40273-024-01363-1

Access Statistics for this article

PharmacoEconomics is currently edited by Timothy Wrightson and Christopher I. Carswell

More articles in PharmacoEconomics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:pharme:v:42:y:2024:i:5:d:10.1007_s40273-024-01363-1