Cardiometabolic health: evaluating the cost-effectiveness of a diagnostic device
A client wanted us to analyse the cost-effectiveness of a diagnostic device for stroke patients compared to standard treatment. Modelling the cost-effectiveness of any diagnostic device is complex because the value of the diagnostic device is dependent on the value of the treatments available for this condition. This model was particularly challenging because stroke patients can experience a diverse range of acute health events, including recurrent stroke and bleeding, with lifelong consequences.
Together with the client and leading experts in the field, we developed a robust model to analyse several comparators, risk of recurrent stroke and their impact on health and costs. We stress-tested the model results using alternative input values based on extensive review of the literature and expert opinion.
The model that we co-created with the client worked well and was accepted as a publication in a high-impact academic journal. The publication was downloaded 500 times within the first 3 months. We continue to work closely with the client to help them understand the cost-effectiveness of further iterations of this device and other indications.
Dermatology: overcoming the short-term bias in clinical evidence to compare long-term efficacy of psoriasis treatments
Comparing the effectiveness of treatments for moderate-to-severe plaque psoriasis requires overcoming the following challenges:
(1) Although the majority of patients require long-term treatment, most trials investigate the initial “induction phase” of treatment, during the first 10-16 weeks.
(2) The relatively few studies with long-term evidence during the “maintenance phase” of treatment differ substantially in design (e.g. cross-over, withdrawal, re-treatment) and treatments compared.
We wanted to find a scientifically rigorous way to identify all systemic therapies approved for this indication and to compare their efficacy at one year based on the gold-standard Psoriasis Area and Severity Index (PASI).
Building on our previous work, which we published in a high-impact journal and has been cited over 50 times, we identified, synthesised, and analysed studies published between 2000 and 2018 according to PRISMA guidelines. We comprehensively assessed the studies for heterogeneity that might confound our comparisons. This led us to perform a hierarchical Bayesian network meta-analysis of PASI using an ordered probit model to estimate probabilities that patients would achieve PASI scores of 75, 90 or 100.
Using this thorough review, we arrived at two clarifying discoveries. One was that placebo cross-over after the induction phase turned many studies into essentially one-arm trials. The other was that PASI in the placebo group appeared to plateau in the transition from the induction to maintenance phase of treatment. This led us to propose two analyses, which we validated with leading psoriasis clinicians:
(1) One analysis would compare outcomes at 52 weeks across 9 studies that reported complete placebo and treatment data out to that time point.
(2) A second analysis would assume that placebo response during a maintenance phase would remain the same as during the induction phase. This assumption of a “placebo plateau” allowed us to compare placebo and treatment data out to 52 weeks in a network of 28 studies, instead of 9.
Our work provided a rigorous foundation for our clients to compare their product with all available treatments for moderate-to-severe plaque psoriasis, and these comparisons could support major R&D decisions. The work was subsequently published in three highly ranked journals and presented at a major international meeting. Our work has proven extremely relevant to researchers, clinicians and regulators: the Professional Society for Health Economics and Outcomes Research (ISPOR) commended our approach in 2020, creating an industry-wide precedent for how healthcare stakeholders can overcome evidence gaps that interfere with health economics and outcomes research.
Oncology: overcoming limited evidence to gain reimbursement for a blockbuster oncology drug
A major global drug manufacturer wished to achieve reimbursement in the UK and the return on investment for its promising drug against breast cancer. Our client faced a challenge, however: abundant evidence showed that their drug slowed cancer progression, but not enough data were available to determine whether the drug improved overall survival. This task was especially difficult because the client was proposing the drug as a first-line treatment, yet patients might use several downstream drugs as their disease progressed, confounding survival outcomes.
To prepare for the reimbursement submission, we completed a gap analysis of the existing evidence and compiled a list of its strengths and weaknesses. We explored multiple methods for linking the progression evidence to patient survival. In the end, we developed a model that drew on the difference in progression between the drug and control arms, as well as data on sequential oncology treatments. We successfully argued that despite the uncertainty due to confounding from the use of multiple medications during progression, the ability of the client’s drug to slow progression suggested a positive impact on patient survival.
We aligned the client’s global executive team and the local market access colleagues behind a submission that ultimately convinced three healthcare payers of the drug’s value (National Institute of Clinical Excellence, Scottish Medicines Consortium and the Irish National Centre for Pharmacoeconomics). As a result, the treatment was made available within the client’s desired pricing strategy – making the drug the first alternative to hormone therapy to become available in the last 10 years to women with breast cancer. Since regulatory agencies did not raise any concerns and no protracted negotiations over pricing were necessary, several thousand patients were able to benefit immediately from the treatment, substantially boosting the client’s market share.
Policy and methods: validating comparisons of disease treatments in the absence of head-to-head trials
We believe that good consulting practice means identifying industry needs and seeking to address them. From our long experience in the field of psoriasis, we were aware that most clinical trials for new agents use placebo rather than other agents as the comparator. Thus, most comparisons of treatment efficacy have relied on network meta-analyses (NMAs). We wondered: what if NMAs were not giving a reliable picture? We wanted to ensure that clinical and regulatory decisions would be based on solid evidence.
We systematically searched the literature for NMAs comparing at least two biologics to treat moderate-to-severe psoriasis. We compared the quality, methodology and funding sources across 18 NMAs, and we focused on two clinical outcomes common across the studies. We found that all the NMAs came to similar conclusions despite differences in methodology and sources of funding.
Our work, which supported the validity of NMAs for treatment comparisons in moderate-to-severe psoriasis, led to a publication in a highly ranked journal and a presentation at a global conference. Our clients with a strong pipeline in skin diseases were able to use our findings immediately to support major short- and long-term R&D decisions. More broadly, our work has proven extremely relevant to researchers, clinicians and regulators in multiple therapeutic areas where head-to-head treatment comparisons are lacking. Thus, our publication has been accessed 1000 times since 2020.
Rare diseases: modelling long-term survival for rare disease patients after the trial period
Progressive Fibrotic Interstitial Lung disease (PFILD) was a newly identified group of patients that clinicians highlighted as potential beneficiaries of a new drug. As this was a new condition, existing research into these patients’ condition was minimal. One year, of survival data was available from the clinical, but not data were available on survival following the end of the trial.
We identified that patients PFILD were likely to experience similar survival rates to patients with IPF. IPF was a more well-established health condition, with survival data available for a 7 year period.
First, we matched the IPF patients to the PFILD patients.
Second, we used the matched survival data for patients IPF as priors to extrapolate survival for PFILD patients following the one-year clinical trial.
This survival data has now been published and comprises the best available estimate for medium-long term survival for this group of patients. It will play a pivotal role in future cost-effectiveness modelling in this disease area.
Respiratory: gaining reimbursement for a drug targeting a respiratory condition
Idiopathic Pulmonary Fibrosis was historically considered a rare disease, though the prevalence has increased over time. We were tasked with supporting the development of a robust economic evaluation for the second drug to market.
The fact that the condition is rare means that existing research and available data were limited. In particular, there was limited data available on survival, following the one-year clinical trials.
We conduct a systematic review and network meta-analysis feasibility assessment. We used these data to develop a global economic model, which we then adapted first for NICE and subsequently for more than 10 other health systems.
We were able to demonstrate an in depth understanding of the disease area and build a symbiotic relationship with the client. This meant that the client trusted us as a partner to support them through multiple phases of health economic modelling over several years.
The drug gained rapid market access in the UK with minimal ERG feedback. The submission was accepted following its first Appraisal Consultation Document (ACD) saving potentially months of additional negotiation with NICE.
We leveraged our expert knowledge in the disease area and close relationship with the client to develop the submission strategy in partnership.