Difficulties arising in reimbursement recommendations on new medicines due to inadequate reporting of population adjustment indirect comparison methods

Holmes, Eileen; Leahy, Joy; Walsh, Cathal; White, Arthur; Donnan, Peter T.; Lamrock, Felicity

doi:10.1002/jrsm.1368

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…The NCPE Review Group together with the Scottish Medicines Consortium have previously published guidance in this area. 10 A pandemic highlights particular vulnerabilities with this type of evidence because the world in which the study was conducted may not be the same as the world going forward. This means that the outcomes observed in a trial conducted during the pandemic may not be the same as expected in the future.…”

Section: Treatment Effectmentioning

confidence: 99%

Coronavirus Disease 2019: Considerations for Health Technology Assessment From the National Centre for Pharmacoeconomics Review Group

et al. 2020

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It is expected that the coronavirus disease 2019 (COVID-19) pandemic will leave large deficits in the budgets of many jurisdictions. Funding for other treatments, in particular new treatments, may become more constrained than previously expected. Therefore, a robust health technology assessment (HTA) system is vital. Many clinical trials carried out during the pandemic may have been temporarily halted, while others may have had to change their protocols. Even trials that continue as normal may experience external changes as other aspects of the healthcare service may not be available to the patients in the trial, or the patients themselves may contract COVID-19. Consequently, many limitations are likely to arise in the provision of robust HTAs, which could have profound consequences on the availability of new treatments. Therefore, the National Centre for Pharmacoeconomics Review Group wishes to discuss these issues and make recommendations for applicants submitting to HTA agencies, in ample time for these HTAs to be prepared and assessed. We discuss how the pandemic may affect the estimation of the treatment effect, costs, life-years, utilities, discontinuation rates, and methods of evidence synthesis and extrapolation. In particular, we note that trials conducted during the pandemic will be subject to a higher degree of uncertainty than before. It is vital that applicants clearly identify any parameters that may be affected by the pandemic. These parameters will require considerably more scenario and sensitivity analyses to account for this increase in uncertainty.

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Section: Treatment Effectmentioning

confidence: 99%

Coronavirus Disease 2019: Considerations for Health Technology Assessment From the National Centre for Pharmacoeconomics Review Group

et al. 2020

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“…Such a poor reporting practice implies a lack of transparency and reduces the reproducibility of the conducted STC or ML-NMR analysis. As criticized by Holmes et al (2019), this practice also increases the uncertainty in the decision making process of regulatory committees when handling a new drug/indication application. 9 In addition, the clinical relevance of the PAIC findings (especially compared to before adjustment) was not often T A B L E 4 Methodological recommendations for population-adjusted indirect comparisons (PAICs).…”

Section: Discussionmentioning

confidence: 99%

“…These are critically important because studies included in a PAIC analysis are also required to be of high methodological quality, or to be relatively homogeneous in the eligibility criteria, in the common comparator (for anchored comparisons) and in the outcome measurement. Besides, adequate reporting of the modeling strategy and of the model fitting results (e.g., in STC and ML‐NMR) is essential to assess the validity of the obtained findings, 7,9 but this was not assessed in Reference 4.…”

Section: Introductionmentioning

confidence: 99%

Population adjusted‐indirect comparisons in health technology assessment: A methodological systematic review

Truong

Tran

Lê

et al. 2023

Research Synthesis Methods

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In health technology assessment (HTA), population‐adjusted indirect comparisons (PAICs) are increasingly considered to adjust for the difference in the target population between studies. We aim to assess the conduct and reporting of PAICs in recent HTA practice, by performing, a methodological systematic review of studies implementing PAICs from PubMed, EMBASE Classic, Embase/Ovid Medline All, and Cochrane databases from January 1, 2010 to Feb 13, 2023. Four independent researchers screened the titles, abstracts, and full‐texts of the identified records, then extracted data on methodological and reporting characteristics of 106 eligible articles. Most PAIC analyses (96.9%, n = 157) were conducted by (or received funding from) pharmaceutical companies. Prior to adjustment, 44.5% of analyses (n = 72) (partially) aligned the eligibility criteria of different studies to enhance the similarity of their target populations. In 37.0% of analyses (n = 60), the clinical and methodological heterogeneity across studies were extensively assessed. In 9.3% of analyses (n = 15), the quality (or bias) of individual studies was evaluated. Among 18 analyses using methods that required an outcome model specification, results of the model fitting procedure were adequately reported in three analyses (16.7%). These findings suggest that the conduct and reporting of PAICs are remarkably heterogeneous and suboptimal in current practice. More recommendations and guidelines on PAICs are thus warranted to enhance the quality of these analyses in the future.

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“…More detailed methods for the identification and selection of the prognostic factors and treatment effect modifiers, in addition to the model specification and selection process, are required to improve the transparency in combination with broader access to IPD. 66 An alternative approach to an unanchored MAIC would be to perform an outcomes-regression based indirect comparison or simulated treatment comparison (STC), based on a regression model for the index intervention for the outcome of interest as a function of relevant patient-related factors using the IPD of the index trial (ie, typically manufacturer trial [s]). This model could then be refit using the same IPD by centering the covariates at the mean values of the competing disconnected studies (for which only aggregate study-level data is available) to obtain the population-adjusted estimates with the index intervention for a target population as well as each of the competitor trials (ie, single-arm or disconnected RCTs).…”

Section: Discussionmentioning

confidence: 99%

Critical Appraisal of Published Indirect Comparisons and Network Meta-Analyses of Competing Interventions for Multiple Myeloma

et al. 2020

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Objectives: In the field of relapsed or refractory multiple myeloma (RRMM), between-trial or indirect comparisons are required to estimate relative treatment effects between competing interventions based on the available evidence. Two approaches are frequently used in RRMM: network meta-analysis (NMA) and unanchored matching-adjusted indirect comparison (MAIC). The objective of the current study was to evaluate the relevance and credibility of published NMA and unanchored MAIC studies aiming to estimate the comparative efficacy of treatment options for RRMM.Methods: Twelve relevant studies were identified in the published literature (n = 7) and from health technology assessment agencies (n = 5). Data from trials were extracted to identify between-trial differences that may have biased results. Credibility of the performed analyses and relevance of the research questions were critically appraised using the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) checklist and feedback based on consultations with clinical experts. Results:The identified studies concerned NMAs of randomized controlled trials (RCTs; n = 7), unanchored MAICs (n = 4), or both types of analyses (n = 1). According to clinical expert consultation, the majority of the identified NMAs did not consider differences in prior therapies or treatment duration across the RCTs included in the analyses, thereby compromising the relevance. Conclusion:Based on the results and feedback from clinicians, the majority of NMAs did not consider prior treatment history or treatment duration, which resulted in nonrelevant comparisons. Furthermore, it may have compromised the credibility of the estimates owing to differences in effect-modifiers between the different trials. Pairwise comparisons by means of unanchored MAICs require clear justification given the reliance on non-randomized comparisons.

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Difficulties arising in reimbursement recommendations on new medicines due to inadequate reporting of population adjustment indirect comparison methods

Cited by 5 publications

References 5 publications

Coronavirus Disease 2019: Considerations for Health Technology Assessment From the National Centre for Pharmacoeconomics Review Group

Coronavirus Disease 2019: Considerations for Health Technology Assessment From the National Centre for Pharmacoeconomics Review Group

Population adjusted‐indirect comparisons in health technology assessment: A methodological systematic review

Critical Appraisal of Published Indirect Comparisons and Network Meta-Analyses of Competing Interventions for Multiple Myeloma

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