Quantitative Evidence Synthesis Methods for the Assessment of the Effectiveness of Treatment Sequences for Clinical and Economic Decision Making: A Review and Taxonomy of Simplifying Assumptions

Development of a Decision Model to Estimate the Outcomes of Treatment Sequences in Advanced Melanoma

BackgroundA decision model for patients with advanced melanoma to estimate outcomes of a wide range of treatment sequences is lacking.ObjectivesTo develop a decision model for advanced melanoma to estimate outcomes of treatment sequences in clinical practice with the aim of supporting decision making. The article focuses on methodology and long-term health benefits.MethodsA semi-Markov model with a lifetime horizon was developed. Transitions describing disease progression, time to next treatment, and mortality were estimated from real-world data (RWD) as a function of time since starting treatment or disease progression and patient characteristics. Transitions were estimated separately for melanoma with and without a BRAF mutation and for patients with favorable and intermediate prognostic factors. All transitions can be adjusted using relative effectiveness of treatments derived from a network meta-analysis of randomized controlled trials (RCTs). The duration of treatment effect can be adjusted to obtain outcomes under different assumptions.ResultsThe model distinguishes 3 lines of systemic treatment for melanoma with a BRAF mutation and 2 lines of systemic treatment for melanoma without a BRAF mutation. Life expectancy ranged from 7.8 to 12.0 years in patients with favorable prognostic factors and from 5.1 to 8.7 years in patients with intermediate prognostic factors when treated with sequences consisting of targeted therapies and immunotherapies. Scenario analyses illustrate how estimates of life expectancy depend on the duration of treatment effect.ConclusionThe model is flexible because it can accommodate different treatments and treatment sequences, and the duration of treatment effects and the transitions influenced by treatment can be adjusted. We show how using RWD and data from RCTs can harness advantages of both data sources, guiding the development of future decision models.HighlightsThe model is flexible because it can accommodate different treatments and treatment sequences, and the duration of treatment effects as well as the transitions that are influenced by treatment can be adjusted.The long-term health benefits of treatment sequences depend on the place of different therapies within a treatment sequence.Assumptions about the duration of relative treatment effects influence the estimates of long-term health benefits.We show how the use of real-world data and data from randomized controlled trials harness the advantages of both data sources, guiding the development of future decision models.

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

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.

Cost-Utility Analysis of Rituximab vs Mycophenolate Mofetil for the Treatment of Pemphigus Vulgaris

Importance

There is an increasing body of literature that supports the use of rituximab as a first-line steroid-sparing agent in pemphigus vulgaris. However, the cost of rituximab is substantial compared with conventional agents, and there are limited health economic data to justify its use.

Objective

To evaluate the cost-effectiveness of rituximab biosimilars relative to mycophenolate mofetil as a first-line steroid-sparing agent for moderate to severe pemphigus vulgaris.

Design, Setting, and Participants

A cost-utility analysis over a 24-month time horizon was conducted from the perspective of the Australian health care sector using a modeled cohort of treatment-naive adult patients with moderate to severe pemphigus vulgaris. A Markov cohort model was constructed to simulate disease progression following first-line treatment with rituximab biosimilars or mycophenolate mofetil. The simulated cohort transitioned between controlled disease, uncontrolled disease, and death. Efficacy and utility data were obtained from available published literature. Cost data were primarily obtained from published government data. One-way and probabilistic sensitivity analyses were performed to assess uncertainty. Primary outcomes were the changes in cost and quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (ICER) over the 24 months.

Interventions

Rituximab biosimilars and mycophenolate mofetil.

Results

The simulated cohort of treatment-naive patients had a mean age of 50.8 years, a female-to-male ratio of 1.24, and moderate to severe disease as classified by the Harman criteria. First-line rituximab biosimilars were associated with a cost reduction of AU$639 and an improvement of 0.07 QALYs compared with mycophenolate mofetil, resulting in an ICER of -AU$8818/QALY. Rituximab biosimilars were therefore more effective and less costly compared with mycophenolate mofetil. Sensitivity analyses demonstrated that rituximab biosimilars remained cost-effective across a range of values for cost, utility, and transition probability input parameters and willingness-to-pay thresholds.

Conclusions and Relevance

In this cost-utility analysis, rituximab biosimilars were cost-effective compared with mycophenolate mofetil for moderate to severe pemphigus vulgaris. Further investigation into its cost-effectiveness over a longer time horizon is necessary, but the favorable results of this study suggest that the high acquisition costs of rituximab biosimilars may be offset by its effectiveness and provide economic evidence in support of its listing on the Pharmaceutical Benefits Scheme for pemphigus vulgaris.

Conceptual Framework and Methodological Challenges for Modeling Effectiveness in Oncology Treatment Sequence Models

In this review, we summarize the challenges faced by existing oncology treatment sequence decision models and introduce a general framework to conceptualize such models. In the proposed framework, patients with cancer receive at least two lines of therapy (LOTs) followed by palliative care throughout their lifetime. Patients cycle through progression-free and progressive disease health states in each LOT before death. Under this framework, four broad aspects of modeling effectiveness of treatment sequences need exploration. First, disease progression, treatment discontinuation, and the relationship between the two events should be considered. Second, the effectiveness of each LOT depends on its placement in a treatment sequence as the effectiveness of later LOTs may be influenced by the earlier LOTs. Third, the treatment-free interval (TFI; time between discontinuation of earlier LOT and initiation of later LOT) may impact a therapy's effectiveness. Fourth, in the absence of head-to-head trials directly comparing LOTs, indirect treatment comparison (ITC) of outcomes for a specific LOT or even for the entire treatment sequence is important to consider. A search of decision models that estimated effectiveness of at least two lines of oncology therapy was conducted in PubMed (N = 20) and technology appraisals by the National Institute for Health and Care Excellence (N = 26) to assess four methodological aspects related to the model framework: (1) selection of outcomes for effectiveness in a treatment sequence, (2) approaches to adjust the efficacy of a treatment in consideration of its place in the sequence, (3) approaches to address TFIs between LOTs, and (4) incorporation of ITCs to estimate comparators' effectiveness in the absence of direct head-to-head evidence. Most models defined health states based on disease progression on different LOTs while estimating treatment duration outside of the main model framework (30/46) and used data from multiple data sources in different LOTs to model efficacy of a treatment sequence (41/46). No models adjusted efficacy for the characteristics of patients who switched from an earlier LOT to a later LOT or adjusted for the impact of prior therapies, and just six models considered TFIs. While 11 models applied ITC results to estimate efficacy in comparator treatment sequences, the majority limited the ITC to one LOT in the sequence. Thus, there is substantial room to improve the estimation of effectiveness for treatment sequences using existing data when comparing effectiveness of alternative treatment sequences.

Defining the decision problem: a scoping review of economic evaluations for Clostridioides difficile interventions

BACKGROUND

Clostridioides difficile infection is the leading cause of healthcare-associated infectious diarrhoea. Several preventative and treatment interventions exist; however, decisions for their use are typically made independent of other interventions along the care pathway.

AIM

To assess how the scope of the decision problem is defined in economic evaluations of C. difficile interventions.

METHODS

A scoping review was conducted following the Joanna Briggs Institute framework using a comprehensive literature search with C. difficile and economic evaluation as key search concepts. Study selection and extraction were performed independently by two reviewers. An in-depth analysis of all cost-utility and cost-effectiveness analyses was conducted. Care pathway domains (i.e. infection prevention and control, antimicrobial stewardship programmes, prevention, diagnostics, treatment) were defined iteratively, and each study was classified according to the scope of the decision problem: (i) one intervention, one domain; (ii) one intervention, multiple domains; (iii) multiple interventions, one domain; and (iv) multiple interventions, multiple domains.

RESULTS

In total, 3886 studies were identified. Of these, 116 studies were included in the descriptive overview, and 46 were included in the in-depth analysis. Most studies limited the scope of the decision problem to one intervention (43/46; 93%). Only three studies (3/46; 7%) assessed multiple interventions - either as bundled vs standalone interventions for prevention (i.e. a single domain), or as sequences of treatments for initial and recurrent infection (i.e. multiple domains). No study assessed multiple interventions across prevention and treatment domains.

CONCLUSIONS

Economic evaluations for C. difficile infection assess narrowly defined decision problems which may have implications for optimal healthcare resource allocation.