Renal cell carcinoma pathway model report

Patient experts explained that advanced RCC is life changing. They explained how RCC affects people's lives, starting from the shock and despair of initial diagnosis. It is difficult for people with RCC to continue with daily life even after successful treatment, because of the fear of disease recurrence. Patient experts said that people with advanced and metastatic RCC are frequently hospitalised, may have to take early retirement and have uncertainty about the future. Commonly there is a significant psychological impact. Patient experts explained that current treatment options are associated with toxicity, which can result in needing to take time off work. There is inconsistency in which treatment options are available across the country, and for some people there are no treatment options at all. The committee concluded that advanced RCC has a large impact on quality of life.

Ipsen accepted that the EAG's model was flexible to model up to 4 lines of treatment before best supportive care. But noted that most costs and benefits are accumulated in the first 2 lines of treatment. They considered that the model should only explicitly consider 2 lines of treatment and argued that this would be consistent with previous NICE technology appraisals in RCC. The EAG explained that only a small proportion of time is spent having third- and fourth-line treatments, which matches expectations for NHS practice. A patient expert commented that the state transition model should consider the entire pathway and that 4 lines of treatment before best supportive care was more appropriate. This will more accurately capture the current treatment pathway, but also ensure the model can change in the future should the pathway change or new treatments become available at third or fourth line. A clinical expert agreed and considered that other technologies could be licensed after second line. So, it would be an advantage to be able to include these in the model efficiently. The committee concluded that, even though most QALYs were accrued in the first and second lines of treatment, it is a strength of the analysis that the model has the option to consider later lines more granularly until the time horizon. The committee preferred the pathway model to include up to 4 lines of treatment followed by best supportive care.

The committee considered the EAG's sources used to inform clinical effectiveness. Systematic literature reviews were done following NICE's manual on health technology evaluation to identify randomised controlled trials and real-world evidence for advanced RCC. The EAG considered evidence submitted by companies, professional and patient organisations. The committee agreed with the approach and the data sources included to inform the clinical-effectiveness evidence. See section 3 of the EAG's assessment report for more details of the search and selection process and results.

The committee understood that the clinical evidence base across the risk groups has limitations. For example, some trials only reported the overall population and differences in available treatments, because the pathway has changed over time (newer studies include more novel treatments than older studies). The committee concluded that, despite the limitations, the network meta-analyses included all appropriate data for the pathway model approach, and that the limitations related generally to a changing evidence base. The committee recommended that future RCC trials should be sufficiently powered to analyse differences in treatment effect by risk group (see recommendations for research). See section 3.3 of the assessment report for more details on limitations of the network meta-analyses.

The EAG only considered 1 out of 12 real-world datasets identified in the systematic review to be robust and relevant to the UK (Challapalli et al. 2022). The dataset owners gave the EAG access to unpublished patient level data. The data contained IMDC risk scores, treatment patterns, overall survival, progression-free survival, treatment stopping, time to next treatment, time to progression and relative dose intensity. The model uses real-world evidence from Challapalli for the baseline characteristics, natural history, treatment pathway and sequences. Sunitinib data from Challapalli was used to inform a reference curve for the baseline risk for people having first-line treatment. Cabozantinib data was used to inform second-line treatment, and beyond. These reference curves are the foundation of baseline risk in the model. The committee considered that clinical trial populations are often substantially different to people in clinical practice. Most clinical trials recruit people with higher performance status and people have more monitoring than in clinical practice. The committee also noted that nephrectomies are more common in trials than in practice, with about 70% of people in CheckMate 9ER having a nephrectomy compared with 54% in Challapalli. The committee agreed that Challapalli is likely to be representative of people having treatment for RCC in the UK. It explained that Challapalli provides a good source of evidence for baseline characteristics, and agreed it is good practice to use evidence that reflects NHS practice. The committee also explained that the dataset provides a good indication of the likely treatment sequences that the pathway will rely on. See section 3.6 of the EAG's assessment report for more details on the real-world evidence.

The trials from the systematic literature reviews (see section 1.8) were used to inform network meta-analyses for clinical outcomes to be used in the model. Network meta-analyses were done using the first-line networks for the all-risk group, favourable-risk, and intermediate- or poor-risk subgroups. The second and subsequent line network was used for a network meta-analysis only for the all-risk group. Networks were formed for overall survival, progression-free survival, overall response rate, stopping because of adverse events and the risk of treatment-emergent adverse events of grade 3 or higher. The committee concluded the networks were appropriate and considered all relevant outcomes and treatments in the pathway. See section 3.7 and appendix E of the EAG's assessment report for more details on the network meta-analyses.

The relative effectiveness from network meta-analyses were applied to the reference curves. For first-line treatments, the relative effects calculated in first-line network meta-analyses are applied to the sunitinib reference curves. For second- and later-line treatments, the relative effects calculated in second- and later-line network meta-analyses are applied to the cabozantinib reference curves. The EAG did network meta-analyses for overall survival and progression-free survival for the all-risk group, and the favourable-risk and intermediate- and poor-risk subgroups. The EAG also did a safety network meta-analysis for the all-risk group. The committee acknowledged that network meta-analysis methods were necessary to be able to compare to all treatments in the pathway.

Intermediate- or poor-risk progression-free survival data for pembrolizumab plus lenvatinib at first line was missing because of confidentiality marking. So, a proportional hazards model was used to extrapolate pembrolizumab with lenvatinib. A fractional polynomials model was tested for completeness. Without data for intermediate- and poor-risk RCC, the EAG used data from the all-risk group for pembrolizumab plus lenvatinib. The EAG explained that this provided implausible hazards for pembrolizumab plus lenvatinib. The EAG acknowledged that using a proportional hazards approach for pembrolizumab plus lenvatinib alone might bias results in favour of pembrolizumab plus lenvatinib. But it considered that applying treatment effect waning (see section 1.23) would largely mitigate this bias. The committee noted this but would have still preferred to see a consistent fractional polynomial approach applied to all treatments. The committee requested that additional fractional polynomials analyses be provided for pembrolizumab plus lenvatinib, either with the missing progression-free survival data from the intermediate- or poor-risk subgroup, or with alternative assumptions (such as assuming a consistent hazard shape or shared parameter with a drug of the same class).

The committee considered CABOSUN's inclusion in the network meta-analyses. CABOSUN is an older trial, with a small population that included only intermediate- and poor-risk RCC. The committee observed that the progression-free survival and overall survival results for sunitinib reported in CABOSUN were lower than in other trials. It noted that this could have been because of the population, and because immunotherapies were not available when CABOSUN was done. The committee considered that including CABOSUN in the network meta-analysis could overestimate the relative treatment effect of cabozantinib. The committee concluded it was cautious about using the CABOSUN trial in the network meta-analyses, which added uncertainty to the results for cabozantinib.

Unlike progression-free survival and overall survival outcomes, there was insufficient published trial data on time to progression, time to next treatment and time to stopping treatment to inform standalone networks for these outcomes. The EAG did a targeted review to investigate the plausibility of surrogacy between progression-free survival, time to stopping treatment, and time to next treatment. Based on this review, the EAG applied hazard ratios from the progression-free survival network meta-analysis to the time to stopping treatment and time to next treatment reference curves. It did this to estimate time to stopping treatment and time to next treatment for other treatments. Ipsen considered that there were lots of assumptions involved in generating time to stopping treatment estimates and suggested a simplification in which time to stopping treatment is assumed to be equal to progression-free survival. The committee considered this but noted that, while simpler, assuming that time to stopping treatment was equal to progression-free survival was a strong assumption. The committee considered the evidence and observed that there was moderate to high correlation between progression-free survival and both time to next treatment and time to stopping treatment for most comparators. It noted that for nivolumab plus ipilimumab the relationship was less clear. The clinical expert explained that time to stopping treatment, time to progression and progression-free survival are not always similar, especially with immunotherapies. This is because some people may stop treatment because of adverse events but may still benefit from the treatment for some time. In these situations, time to stopping treatment will be somewhat shorter than progression-free survival or time to progression. The committee noted that if time to stopping treatment was assumed to be equal to progression-free survival , the off-treatment health states effectively disappeared from the model. It considered that setting time to stopping treatment and time to progression equal to progression-free survival would bias the results of the model. The committee concluded that it preferred to use available time to stopping treatment data and apply progression-free survival network meta-analyses to the time to stopping treatment and time to progression reference curves. See section 4.3.1.2 of the EAG's assessment report for further details.

A key assumption of the state transition model is that progression-free survival is an appropriate surrogate for overall survival. This is because the model is driven by multiple lines of progression-free survival to generate survival and quality-adjusted survival outcomes. So the model requires a surrogate relationship between progression-free survival at each line and overall survival to exist. The committee considered that the available evidence in the literature supported the assumption of surrogacy between progression-free survival and overall survival. But the mechanism of action of some treatments meant that the assumption was sometimes limited. For example, nivolumab plus ipilimumab was seen to have worse progression-free survival in CheckMate 214 than other combination treatments in their pivotal trials, but still has a sustained survival benefit. When considering the most recent publicly available data cut, nivolumab plus ipilimumab had a median progression-free survival of 12.3 months (Motzer et al. 2022) compared with 23.9 months for pembrolizumab plus lenvatinib (see the EAG's assessment report table 14). But, when considering overall survival, this translated to a median overall survival of 55.7 months for nivolumab plus ipilimumab compared with 53.7 months for pembrolizumab plus lenvatinib (see the EAG's assessment report table 13). The EAG explained that this could be caused by tumour flare. This is when tumours increase in size in the initial stages of treatment, resulting in a progression event being recorded, before falling in size as the full treatment effect is realised. Alternatively, it may be because the definition of progression used in CheckMate 214 may not reflect clinical practice. The committee observed that predictions for overall survival generated by the state transition model for nivolumab plus ipilimumab were more pessimistic than those observed in CheckMate 214 and data from the NHS systemic anticancer therapy (SACT) database. This could have been driven by the breakdown of surrogacy between progression-free survival and overall survival for this technology. This was less of an issue when using the partitioned survival method in scenarios as it uses overall survival data directly. This allows the survival benefit seen in CheckMate 214 to be captured. The committee acknowledged that a partitioned survival modelling approach has limitations compared with a state transition approach. These include reduced flexibility, limited ability to capture later-line costs and benefits, and the need to make other strong assumptions that could lead to additional uncertainty. The EAG also presented a scenario in which time to next treatment was used as a proxy for progression-free survival for nivolumab plus ipilimumab. The EAG argued this might better reflect expected overall survival for nivolumab plus ipilimumab. The committee concluded that when there is evidence of poor surrogacy between progression-free survival and overall survival for a treatment in the model, alternative ways of driving health state occupancy should be explored.

The EAG applied treatment effect waning to the hazards of all immunotherapy and TKI combinations at 5 years. This time point was selected based on how long people have these immunotherapies in clinical practice, in which stopping rules are in place. Five years was the longest timepoint when data was available with a reasonable number at risk. The committee agreed that these assumptions were reasonable, but would have preferred to see real world evidence to justify treatment effect waning assumptions. See section 4.3.5.3 of the EAG's assessment report for more details on treatment effect waning.

The model includes cost and outcomes for up to 3 lines of subsequent treatment. The model assumes that the type of subsequent treatment is independent of the risk group modelled at first line but is dependent on what treatment was had. Clinical advice and routine commissioning rules were used to determine the plausible sequence after each possible treatment at first, second and third line. Proportions of each treatment observed in the real-world evidence were used to capture subsequent treatments in the model. When a subsequent treatment was implausible, the proportion was set to 0 and the treatment's shares were reweighted across other plausible options. Clinical experts explained that the proportion of people moving on to each treatment at each line in the real-world evidence was plausible and the treatment rules applied were appropriate. Sequences are less certain at later lines, but the committee concluded that the proportions applied to later lines are appropriate. The data did not include pembrolizumab plus lenvatinib or cabozantinib plus nivolumab. Both treatments did not feature in the real-world dataset because they are not currently NHS standard care. The committee agreed that assumptions used to capture subsequent treatment in the model reflected expected clinical practice. See section 4.3.5.1 of the EAG's assessment report for full details on how clinical effectiveness was modelled for subsequent treatments.

Checkmate 9ER trial data was used to form the baseline adverse event risk in the model for cabozantinib plus nivolumab and sunitinib at first line. Everolimus data from CheckMate 025 was used for the baseline adverse event risk at second and third line. Hazard ratios from the adverse event proportional hazards grade 3 or more adverse event network meta-analysis were applied to sunitinib data at first line and everolimus data at later lines to estimate adverse events rates for other treatments in the model. Three additional adverse events: hand-foot syndrome, diarrhoea and fatigue not seen in CheckMate 9ER were also included, informed by clinical advice and supported by a Cochrane review. The committee considered that clinical trial data might underestimate the incidence of adverse events compared with clinical practice, but acknowledged that these limitations are a feature of the available data. The committee explained that a naive comparison was not appropriate and preferred the network meta-analysis approach to model adverse events. See section 4.3.6 of the EAG's assessment report for more details on how adverse events were modelled.

The committee considered the severity of the condition (the future health lost by people living with the condition and having standard care in the NHS). The committee may apply a greater weight to QALYs (a severity modifier) if technologies are indicated for conditions with a high degree of severity (a severity modifier). The committee considered absolute and proportional QALY shortfall estimates in line with NICE's manual on health technology evaluation. It noted that the severity of the condition depends on which treatment was considered standard care and there are a range of treatments recommended for untreated advanced RCC. The committee was presented with 3 options for assessing whether a severity weighting applied. These were fully incremental analyses, pairwise analyses (in which the most appropriate comparators were defined) and a weighted market share approach. The committee considered that a fully incremental analysis would be the most suited to optimising the treatment pathway, but it recognised that technology appraisals recommend new treatment as 'options'. So, for some technology appraisals, a pairwise analysis could be appropriate if there are defined clinical reasons why specific comparisons should be made, or to consider comparators that are likely to be displaced. The committee explained that all judgements on severity were based on the current model base case and that other analyses made as the pathway evolves and the model is developed may result in different severity conclusions.