NUMERO DE DIAS CON LLUVIAUBICACIÓN
3.2 Resultado del análisis de correlación múltiple S PSS
Two de novo decision models were developed to assess the cost-effectiveness of CAR T-cell therapy within the two separate TPPs (bridge to HSCT and curative intent) across each of the separate evidence sets. Although a number of common inputs and assumptions were employed across both models, the two
TABLE 22 Model inputs: utilities
Parameter Utility (95% CI) Source/assumption
Treatment disutilities
HSCT disutility 0.57 for 1 year (0.33 to 0.87) Sunget al.:206
‘disutility of undergoing BMT’expert VAS elicitation Adverse events
CRS 0 for 1 week Assume severity of ICU hospitalisation associated with utility of 0 Short-term utility
Relapse 0.75 (0.44 to 1) Kellyet al.:204
‘in the state of relapse’mapped value from CHRIs to EQ-5D
Remission 0.91 (0.87 to 0.95) Kellyet al.:204
‘cured after relapse-all relapsed patients treated with CRT’mapped value from SF-36 to HUI2; need to assume no long-term disutility adverse events from CRT
Long-term utility
Long-term disutility Remission utility (0.91) with age-adjusted decrement
To reflect ageing of cohort BMT, bone marrow transplantation; CRT, cranial radiation therapy; ICU, intensive care unit.
models had important structural differences that led to differences both in the underlying modelling approach and in the use of external evidence.
In the bridge to HSCT scenario, the primary health benefits of treatment with CAR T-cell therapy were assumed to be driven by an increase in the proportion of patients receiving HSCT and the subsequent success of HSCT itself (based on remission and MRD status). The introduction of an epidemiological‘link’ between a potential established surrogate outcome and/or process (i.e. MRD and HSCT status) and final health benefits (i.e. OS and QALYs) also enabled the use of external evidence to be utilised alongside the separate hypothetical evidence sets generated. A landmark response model was developed utilising evidence from the hypothetical evidence sets to inform short-term outcomes of remission, HSCT and MRD status and external evidence to estimate OS conditional on these shorter-term outcomes. Hence, the key assumption employed within this scenario is that external evidence substantiating the relationship between MRD and HSCT status in studies in which CAR T-cells have not been used can be generalised to patients in whom CAR T-cells have been used. Importantly, the results of our validation work appear to demonstrate that, with minor calibration and adjustment, the combination of trial-reported evidence on short-term outcomes (remission, HSCT and MRD status) and external evidence on their relationship to OS appeared to closely match the OS estimates directly reported within the studies used to generate the evidence sets for CAR T-cell therapy and the comparator (clofarabine).
In the curative intent model a different assumption was employed, specifically that the CAR T-cell therapy itself potentially confers longer-term and potentially curative benefits without the need to bridge to HSCT. In this context, the case for use of a structural link between final health benefits and a surrogate outcome or process such as HSCT appears more limited. Instead, a simple three-state partitioned survival model was developed to model long-term outcomes through the direct extrapolation of OS data from the evidence sets. An important consideration within this model was whether or not the use of conventional parametric survival functions (e.g. exponential, Weibull, log-normal) would adequately capture the potential for a less conventional hazard function that might be observed for a curative treatment and how this might be affected by different levels of precision and maturity of evidence. Consequently, our work considered the goodness of fit of conventional survival functions and more flexible survival models (e.g. spline-based models developed by Royston and Parmar192). A key finding was that the more flexible survival models
appeared to more closely approximate the observed hazard function across each of the evidence sets. To our knowledge, although the use of these more flexible survival models is briefly discussed within existing NICE technical support documents,207we are not aware of any examples of their use to date by
manufacturers or AGs within the NICE TA process. Consequently, further research may be required to more formally consider the appropriateness of alternative survival modelling approaches to regenerative medicines and cell-based therapies, including more flexible models and cure fraction models.208
The importance of the level of data maturity in deriving robust survival projections for the economic model was evident in our results. Although the‘best-fitting’spline models appeared to generate a robust fit to the data over the first 3 months of the KM estimate used in the minimum data set, the functions were not able to accurately predict the tail of the distribution. Furthermore, considerable variation was evident in the predicted long-term survival of the modelled cohort, with a significant spread in the projected survival trajectories employing different parametric functions. Consequently, we concluded that it was unlikely that a single survival distribution could adequately characterise uncertainties over the longer-term extrapolation period. Although the robustness of the ICER estimates to alternative distributions can be explored in separate sensitivity analyses or scenarios, concerns may exist regarding the transparency of subsequent decisions if the weighting of these is not explicitly specified in subsequent policy decisions.
To more formally account for the uncertainty surrounding choice of survival distribution, a model-averaging approach was adopted. This technique involves the parameterisation of uncertainty surrounding the choice of distribution, combining results from a series of alternative survival functions as part of a weighted distribution. This approach samples both the parametric uncertainty associated within each distribution and the uncertainty (or weights) surrounding the choice of preferred method. Through the probabilistic analysis,
it is therefore possible to estimate the joint distribution of uncertainty around the parameter estimates and the choice of survival function.
In contrast to the minimum set, the additional data maturity in the intermediate and mature evidence sets results in greater certainty over the long-term survival benefits of treatment. This leads to reduced variability in the potential trajectories for the survival benefits of treatment. In addition, with more mature evidence, the fitted survival models are better able to predict the tail of the KM curve. Therefore, unlike the bridge to HSCT model, additional evidence maturity in the curative model leads to different projections of survival benefit, as well as impacting on the parametric uncertainty surrounding model extrapolations. The weights in the exemplar model were based on standard measures of statistical fit. However, these weights could also be informed by clinical judgement and the committee’s deliberations.
Given the inevitable uncertainties that are likely to exist regarding the longer-term benefits of regenerative medicines and cell-based therapies and their implications for the robustness of subsequent cost-effectiveness estimates, further methodological research could be usefully undertaken to help inform how these
uncertainties might be appropriately quantified in a transparent manner to inform subsequent decisions. A key consideration here would be the extent to which these weights can be defined prior to the
committee’s deliberations or whether they should be more directly informed by them. Given the potential complexity in both undertaking these analyses and communicating the results, more efforts should be made to ensure that informal judgements can be more explicitly incorporated in a timely and transparent manner.209
A key assumption employed within both models is that from year 5 onwards all patients who remained alive were assumed to experience a similar mortality risk profile as that of a long-term survivor of ALL. Hence, the mortality risks assumed in both models after year 5 were based on matched general population estimates of the all-cause risk of mortality adjusted for excess morbidity and mortality reported in cohorts of long-term survivors of ALL. As data were not assumed to be available beyond 5 years, it is not possible to determine the possible direction and/or magnitude of any possible bias that this approach might introduce. However, this period is consistently utilised within existing studies of ALL and appears clinically to represent an important time point for patients to reach without subsequent relapse. Hence, for the purposes of extrapolation and the exemplar, it was considered a reasonable basis for informing subsequent longer-term extrapolations. This assumption also impacted on reducing some of the longer-term
uncertainties that would inevitably arise from the extrapolation of the data beyond the maximum reported follow-up across the evidence sets considered for CAR T-cell therapies. Clearly, if additional follow-up data were available, then the validity of such an approach could be more formally considered and any claims of longer-term benefits could be more robustly substantiated.
Our searches to inform other model parameters identified other important uncertainties. The existing HRQoL data on ALL were limited and several assumptions were required. Importantly, no existing CAR T-cell study had incorporated measures of HRQoL that could be considered directly in the model. In the absence of these data, assumptions were made based on external studies to account for the possible magnitude of HRQoL benefits of achieving remission, alongside any negative impacts resulting from the model of therapy (i.e. HSCT, chemotherapy) and other specific adverse events. Our model focused
specifically on the impact of CRS and B-cell aplasia. Importantly, no studies were identified on the potential HRQoL impact of these specific events, which are likely to be associated with CAR T-cell therapy,
necessitating the use of potentially arbitrary assumptions. Further research to generate more robust estimates of HRQoL appropriate for cost-effectiveness analysis is clearly required, together with more specific research that more formally demonstrates the impact of specific therapeutic modalities (including CAR T-cell therapy).
Finally, our research also identified important uncertainties regarding both the likely acquisition costs of CAR T-cells and the other key elements of the process (e.g. costs of leukapheresis, costs of conditioning therapies, level of hospitalisation required for different aspects such as conditioning, subsequent
administration and monitoring costs). Furthermore, no account was taken of the potential costs incurred by patients and their families. Based on previous NICE TA appraisals, additional evidence would need to be provided by manufacturers to more robustly determine the potential costs to the NHS to avoid these uncertainties regarding the costing assumptions being raised. An important uncertainty identified related to the costs of HSCT and any additional costs that may arise from longer-term management of patients. A variety of possible sources were identified in our review and important differences were observed across these. Further studies would be useful to more formally cost the short-term and longer-term implications of HSCT in paediatric populations and also to determine the generalisability of studies reporting estimates from outside the UK.
Although the existence of possible learning curves was identified as an important issue in the conceptual review, these were not directly considered within the exemplar. Some aspects of these may become more apparent as larger studies report, particularly those involving centres with different levels of expertise. Hence, some aspects of learning may be reflected within the results from larger studies and/or specific factors may become more apparent in terms of how these might be incorporated within cost-effectiveness assessments. For example, as experience with using CAR T-cell therapies develops, this may have important implications for both the identification and the management of potential adverse events, as well as the provision of the therapy itself. An assumption is made in the exemplar model that the different stages of CAR T-cell therapy would require separate hospitalisations (i.e. for the initial conditioning therapy and later for the subsequent administration of the CAR T-cells and ensuring monitoring). However, as experience and knowledge continues to develop, aspects of the process may evolve over time such that the subsequent administration and monitoring may be undertaken in a less resource-intensive setting. Although the existence of learning curves has received significant attention in the clinical literature, to date, their implications for and
application within cost-effectiveness analysis remain limited and warrant further investigation.133
Finally, an important assumption made within the exemplar relates to the acquisition cost of CAR T-cell therapy itself. In the absence of a commercially available product and published price, an assumption was made that the manufacturer would employ a value-based approach to its decision such that the resulting cost-effectiveness (ICER) estimate was close to NICE’s cost-effectiveness threshold. In the context of the exemplar, this was assumed to be based on the maximum range of the threshold considered by NICE, assuming that the existing EoL criteria are met. Importantly, this price is not considered to be indicative of the final acquisition cost that might be set when commercially available products are available. Neither are we making the assumption that NICE’s current EoL criteria would apply. Rather, the basis for setting the price using the existing cost-effectiveness threshold was to enable different interested parties to better understand the potential impact of other uncertainties (e.g. precision and maturity of evidence) within NICE’s current decision-making process, identifying potential trade-offs that may exist and illustrating how these uncertainties might be more explicitly addressed within different MEAs (i.e. evidence generation and/or pricing schemes). Although it is clearly possible to examine a range of different possible prices for the CAR T-cell therapy within the exemplar, it was considered that this approach may result in the subsequent panel decision process becoming unmanageable (i.e. multiple pricing scenarios) and would lessen the generalisability learning that the exemplar was developed to highlight.