Survival Extrapolation Research Articles

Evaluation of Flexible Parametric Relative Survival Approaches for Enforcing Long-Term Constraints When Extrapolating All-Cause Survival

ObjectivesParametric models are used to estimate the lifetime benefit of an intervention beyond the range of trial follow-up. Recent recommendations have suggested more flexible survival approaches and the use of external data when extrapolating. Both of these can be realized by using flexible parametric relative survival modeling. The overall aim of this article is to introduce and contrast various approaches for applying constraints on the long-term disease-related (excess) mortality including cure models and evaluate the consequent implications for extrapolation. MethodsWe describe flexible parametric relative survival modeling approaches. We then introduce various options for constraining the long-term excess mortality and compare the performance of each method in simulated data. These methods include fitting a standard flexible parametric relative survival model, enforcing statistical cure, and forcing the long-term excess mortality to converge to a constant. We simulate various scenarios, including where statistical cure is reasonable and where the long-term excess mortality persists. ResultsThe compared approaches showed similar survival fits within the follow-up period. However, when extrapolating the all-cause survival beyond trial follow-up, there is variation depending on the assumption made about the long-term excess mortality. Altering the time point from which the excess mortality is constrained enables further flexibility. ConclusionsThe various constraints can lead to applying explicit assumptions when extrapolating, which could lead to more plausible survival extrapolations. The inclusion of general population mortality directly into the model-building process, which is possible for all considered approaches, should be adopted more widely in survival extrapolation in health technology assessment.

Retrospective Comparison of Survival Projections for CAR T-Cell Therapies in Large B-Cell Lymphoma.

Durable remission has been observed in patients with relapsed or refractory (R/R) large B-cell lymphoma (LBCL) treated with chimeric antigen receptor (CAR) T-cell therapy. Consequently, hazard functions for overall survival (OS) are often complex, requiring the use of flexible methods for extrapolations. We aimed to retrospectively compare the predictive accuracy of different survival extrapolation methods and evaluate the validity of goodness-of-fit (GOF) criteria-based model selection for CAR T-cell therapies in R/R LBCL. OS data were sourced from JULIET, ZUMA-1, and TRANSCEND NHL 001. Standard parametric, mixture cure, cubic spline, and mixture models were fit to multiple database locks (DBLs), with varying follow-up durations. GOF was assessed using the Akaike information criterion and Bayesian information criterion. Predictive accuracy was calculated as the mean absolute error (MAE) relative to OS observed in the most mature DBL. For all studies, mixture cure and cubic spline models provided the best predictive accuracy for the least mature DBL (MAE 0.013‒0.085 and 0.014‒0.128, respectively). The predictive accuracy of the standard parametric and mixture models showed larger variation (MAE 0.024‒0.162 and 0.013‒0.176, respectively). With increasing data maturity, the predictive accuracy of standard parametric models remained poor. Correlation between GOF criteria and predictive accuracy was low, particularly for the least mature DBL. Our analyses demonstrated that mixture cure and cubic spline models provide the most accurate survival extrapolations of CAR T-cell therapies in LBCL. Furthermore, GOF should not be the only criteria used when selecting the optimal survival model.

A cost-effectiveness analysis of avelumab plus best supportive care versus best supportive care alone as first-line maintenance treatment for patients with locally advanced or metastatic urothelial carcinoma in Taiwan.

Patients with locally advanced or metastatic urothelial carcinoma have limited treatment options and a poor prognosis. The JAVELIN Bladder 100 trial showed that avelumab as first-line maintenance plus best supportive care significantly prolonged overall survival and progression-free survival versus best supportive care alone in patients with locally advanced or metastatic urothelial carcinoma that had not progressed with first-line platinum-containing chemotherapy. We assessed whether avelumab plus best supportive care is a cost-effective treatment option versus best supportive care alone in this patient group in Taiwan. A partitioned survival model was used to estimate the costs and effects of avelumab plus best supportive care versus best supportive care alone over a 20-year time horizon from the perspective of Taiwan's National Health Insurance Administration. Patient-level data from JAVELIN Bladder 100 on efficacy, safety, utility, and time on treatment were analyzed to provide parameters for the model. Log-normal and Weibull distributions were used for overall survival and progression-free survival, respectively. Costs of healthcare resources, drug acquisition, adverse events, and progression were identified through publicly available data sources and clinician interviews. The model estimated total costs, life years, and quality-adjusted life years. In the modeled base case, avelumab plus best supportive care increased survival versus best supportive care alone by 0.79 life years (2.93 vs. 2.14) and 0.61 quality-adjusted life years (2.15 vs. 1.54). The incremental cost-effectiveness ratio for avelumab plus best supportive care versus best supportive care alone was NT$1 827 680. Most (78%) of the probabilistic sensitivity analyses fell below three times the gross domestic product per capita. Scenario analysis indicated that life year and quality-adjusted life year gains were most sensitive to alternative survival extrapolations for both avelumab plus best supportive care and best supportive care alone. Avelumab first-line maintenance therapy combined with best supportive care was determined as a cost-effective treatment strategy for patients in Taiwan diagnosed with locally advanced or metastatic urothelial carcinoma that had not progressed with platinum-containing chemotherapy.

INES: Interactive tool for construction and extrapolation of partitioned survival models

BackgroundINES (INteractive model for Extrapolation of Survival and cost) provides an open-access tool powered by R that implements three-state partitioned survival models (PSM). This article describes the properties of the tool, and the situations where INES may or may not be suitable.MethodsINES is designed to be used by investigators or healthcare professionals who have a good grasp of the principles of economic evaluation and understand the strengths and weaknesses of partitioned survival models, but are not sufficiently familiar with a statistical package such as Excel or R to be able to construct and test a de-novo PSM themselves.INES is delivered to the user via a batch file. Once downloaded to the user’s hard drive, it interacts with the user via a portable version of R with web interactivity built in Shiny. INES requires absolutely no knowledge of R and the user does not need to have R or any of its dependences installed. Hence the user will deal with a standalone Shiny app. Inputs (digitalized survival curves, unit costs, posology, hazard ratios, discount rate) can be uploaded from a template spreadsheet.ResultsThe INES application provides a seamlessly integrated package for estimating a set of parametric hazard functions for progression free and overall survival, selecting an appropriate function from this menu, and applying this as an input to a PSM to calculate mean costs and quality-adjusted life years. Examples are given that may serve as a tutorial.ConclusionINES offers a rapid, flexible, robust and transparent tool for parametric survival analysis and calculating a PSM that can be used in many different contexts.

Health Technology Reassessment: Addressing Uncertainty in Economic Evaluations of Oncology Drugs at Time of Reimbursement Using Long-Term Clinical Trial Data.

The evidence base to support reimbursement decision making for oncology drugs is often based on short-term follow-up trial data, and attempts to address this uncertainty are not typically undertaken once a reimbursement decision is made. To address this gap, we sought to conduct a reassessment of an oncology drug (pembrolizumab) for patients with advanced melanoma which was approved based on interim data with a median 7.9 months of follow-up and for which long-term data have since been published. We developed a three-health-state partitioned survival model based on the phase 3 KEYNOTE-006 clinical trial data using patient-level data reconstruction techniques based on an interim analysis. We used a standard survival analysis and parametric curve fitting techniques to extrapolate beyond the trial follow-up time, and the model structure and inputs were derived from the literature. Five-year long-term follow-up data from the trial were then used to re-evaluate the cost-effectiveness of pembrolizumab versus ipilimumab for treatment of advanced melanoma. The best fitting parametric curves and corresponding survival extrapolations for reconstructed interim data and long-term data reconstructed from KEYNOTE-006 were different. An analysis of the 5 year long-term follow-up data generated a base case incremental cost-effectiveness ratio (ICER) that was 28% higher than the ICER based on interim trial data. Our findings suggest that there may be a trade-off between certainty and the ICER. Conducting health technology re-assessments of certain oncology products on the basis of longer-term data availability, especially for those health technology adoption decisions made based on immature clinical data, may be of value to decision makers.

Cost-effectiveness and budget impact of pembrolizumab+axitinib versus sunitinib in patients with advanced clear-cell renal cell carcinoma in the Netherlands.

The phase 3 clinical trial KEYNOTE-426 suggested a higher efficacy regarding overall survival (OS) and progression-free survival (PFS) of pembrolizumab+axitinib compared to sunitinib as a first-line treatment for patients with advanced renal cell carcinoma. In this analysis, the potential cost-effectiveness of this combination treatment versus sunitinib for patients with advanced clear-cell renal cell carcinoma (accRCC) was examined from the societal perspective in the Netherlands. For this analysis, a partitioned survival model was constructed. Clinical data were obtained from the published KEYNOTE-426 trial reports; data on costs and (dis-)utilities were derived from published literature. Costs outside of the healthcare sector included treatment-related travel, informal care and productivity loss. Next to a probabilistic scenario analysis, various scenario analyses were performed that aimed at survival extrapolation, different utility values, treatment duration and drug pricing, as well as restricting the cohort to patients with an intermediate or poor prognosis. Further, a budget impact analysis over three years was conducted, in which a sensitivity analysis concerning ranges in costs and the number of patients was applied. Moreover, a scenario concerning increasing market penetration of pembrolizumab+axitinib up to a market share of 80% in the third year was analyzed. The incremental cost-effectiveness ratio (ICER) of pembrolizumab+axitinib was estimated at €368,396/quality-adjusted life year (QALY) gained, with an incremental QALY gain of 0.55 over sunitinib. The probability of cost-effectiveness at a willingness-to-pay threshold of €80,000/QALY was estimated at 0%, a 50% probability was estimated at €340,000/QALY. Cost-effectiveness was not achieved in any of the applied scenarios. The budget impact over three years amounted to €417.3 million upon instantaneous and full replacement of sunitinib, and to €214.9 million with increasing market penetration. Pembrolizumab+axitinib was not estimated to be cost-effective compared to sunitinib as a first-line treatment for patients with accRCC in the Netherlands from a societal perspective. In none of the analyzed scenarios, cost-effectiveness was achieved. However, price reductions and shorter treatment durations might lead to a more favorable ICER.

Extending Beyond Bagust and Beale: Fully Parametric Piecewise Exponential Models for Extrapolation of Survival Outcomes in Health Technology Assessment

ObjectivesWhen extrapolating time-to-event data the Bagust and Beale (B&B) approach uses the Kaplan-Meier survival function until a manually chosen time point, after which a constant hazard is assumed. This study demonstrates an objective statistical approach to estimate this time point. MethodsWe estimate piecewise exponential models (PEMs), whereby the hazard function is partitioned into segments each with constant hazards. The boundaries of these segments are known as change points. Our approach determines the location and number of change points in PEMs from which the hazard in the final segment is used to model long-term survival. We reviewed previous applications of the B&B approach in National Institute for Health and Care Excellence Technology Appraisals (TAs) completed between July 2011 and June 2017. The time points after which constant hazards were assumed were compared between PEMs and the B&B approaches. When further survival data were published following the original TA, we compared these updated estimates to predicted survival from the PEM and other parametric models adjusted for general population mortality. ResultsSix of the 59 TAs in this review considered the B&B approach. There was general agreement between the location of time points identified through the PEM and the B&B approaches. In 2 of the identified TAs the best fitting model to the data was a no-change-point model. Of the 3 TAs for which further survival data became available, PEM provided the closest prediction for survival outcomes in 2 TAs. ConclusionsPEMs are useful for survival extrapolation when a long-term constant hazard trend for the disease is clinically plausible.

Cost-utility analysis of mogamulizumab in advanced mycosis fungoides and Sézary syndrome cutaneous T-cell lymphoma.

Aim: This study assessed the cost-utility of mogamulizumab, a novel monoclonal antibody, versus established clinical management (ECM) in UK patients in previously treated advanced mycosis fungoides (MF)/Sézary syndrome (SS). Materials & methods: Lifetime partitioned survival model based on overall survival, next treatment-free survival and the use of allogeneic stem cell transplant was developed. Inputs were from the pivotal MAVORIC trial, real-world evidenceand published literature. Extensive sensitivity analyses were conducted. Results: Discounted incremental quality-adjusted life years (QALYs), costs and incremental cost-effectiveness ratio were 3.08, £86,998and £28,233. Results were most sensitive to the survival extrapolations, utilitiesand costs after loss of disease control. Conclusion: Mogamulizumab is a cost-effective alternative to ECM in UK patients with previously treated advanced MF/SS.

MSR29 Application of Bayesian Multi-Parameter Evidence Synthesis to Achieve Clinical Plausibility of Lifetime Survival Extrapolations in First-Line Advanced Non-Small Cell Lung Cancer (aNSCLC)

Lifetime overall survival (OS) extrapolations are required to estimate important differences in costs and benefits between comparators in health economic models. Bayesian multi-parameter evidence synthesis (B-MPES) models for aNSCLC patients receiving second-line nivolumab have previously demonstrated clinically reasonable and stable estimates for long-term OS. Here, we investigate the plausibility of B-MPES predictions for OS in the CheckMate 227 Part 1 trial of nivolumab plus ipilimumab (NIVO+IPI) in aNSCLC patients in the first-line setting and compare with uninformed standard parametric models (SPMs).

EE501 Comparison of Survival Extrapolations Using Early Vs Late Data Cuts from the Echelon-1 Trial in Frontline Advanced Hodgkin Lymphoma

The ECHELON-1 trial (NCT01712490) demonstrated superior progression-free survival for brentuximab vedotin plus doxorubicin, vinblastine, and dacarbazine (A+AVD) versus doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD) in patients with frontline stage III/IV Hodgkin lymphoma with a median follow-up of 2 years and superior overall survival (OS) with a median follow-up of 6 years. The objective of this study was to compare the long-term survival extrapolations using trial data from early vs late datacuts.

Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity.

Aim: This research evaluated standard Weibull mixture cure (WMC) network meta-analysis (NMA) with Bayesian hierarchical (BH) WMC NMA to inform long-term survival of therapies. Materials & methods: Four trials in previously treated metastatic non-small-cell lung cancer with PD-L1 >1% were used comparing docetaxel with nivolumab, pembrolizumaband atezolizumab. Cure parameters related to a certain treatment class were assumed to share a common distribution. Results: Standard WMC NMA predicted cure rates were 0.03 (0.01; 0.07), 0.18 (0.12; 0.24), 0.07 (0.02; 0.15)and 0.03 (0.00; 0.09)for docetaxel, nivolumab, pembrolizumaband atezolizumab, respectively, with corresponding incremental life years (LY) of 3.11 (1.65; 4.66), 1.06 (0.41; 2.37)and 0.42 (-0.57; 1.68). The Bayesian hierarchical-WMC-NMA rates were 0.06 (0.03; 0.10), 0.17 (0.11; 0.23), 0.12 (0.05; 0.20)and 0.12 (0.03; 0.23), respectively, with incremental LY of 2.35 (1.04; 3.93), 1.67 (0.68; 2.96)and 1.36 (-0.05; 3.64). Conclusion: BH-WMC-NMA impacts incremental mean LYs and cost-effectiveness ratios, potentially affecting reimbursement decisions.

Use of Minimal Residual Disease Status to Reduce Uncertainty in Estimating Long-term Survival Outcomes for Newly Diagnosed Multiple Myeloma Patients.

Background: Demonstrating the cost-effectiveness of new treatments for multiple myeloma (MM) often relies on the extrapolation of overall survival (OS) trial data. This method can introduce uncertainty in long-term survival estimates if OS data are immature, as is often the case in newly diagnosed MM (NDMM). We explore the use of the relationship between minimal residual disease (MRD) status and OS to reduce uncertainty of long-term survival outcomes. Objectives: To evaluate if uncertainty in long-term modeled outcomes in NDMM is reduced using a response-based partitioned survival model (PSM), whereby patients were categorized as MRD-positive or -negative, relative to a standard PSM, when OS data are immature. Methods: Standard and response-based PSMs, estimating patient life-years (LYs) over a lifetime horizon, were developed for NDMM patients treated with bortezomib, thalidomide, and dexamethasone (BTd) with or without daratumumab as induction and consolidation therapy. In the standard PSM, LYs were determined by extrapolations from individual patient data from CASSIOPEIA. In the response-based PSM, survival was dependent on MRD status at the time of the response assessment via a landmark analysis. Cox-proportional hazard ratios from external sources and CASSIOPEIA informed the relationship for OS between MRD-positive and MRD-negative, and between patients receiving BTd and daratumumab plus BTd, respectively. Uncertainty was assessed by comparing LYs and OS extrapolations from deterministic and probabilistic analyses. Results: This response-based PSM demonstrated reduced uncertainty in long-term survival outcomes compared with the standard PSM (range across extrapolations of 3.4 and 7.7 LYs for daratumumab plus BTd and BTd, respectively, vs 14.8 and 11.8 LYs for the standard PSM). It also estimated a narrower interquartile range of LYs in the probabilistic analyses for the majority of parametric extrapolations. Discussion: Alternative methods to estimate long-term survival outcomes, such as a response-based PSM, can reduce uncertainty in modeling predictions around cost-effectiveness estimates for health technology assessment bodies and payers, thereby supporting faster market access for novel therapies with immature survival data. Conclusions: Use of MRD status in a response-based PSM reduces uncertainty in modeling long-term survival in patients with NDMM and provides a greater number of clinically plausible extrapolations compared with a standard PSM.

Use of Minimal Residual Disease Status to Reduce Uncertainty in Estimating Long-term Survival Outcomes for Newly Diagnosed Multiple Myeloma Patients

Background: Demonstrating the cost-effectiveness of new treatments for multiple myeloma (MM) often relies on the extrapolation of overall survival (OS) trial data. This method can introduce uncertainty in long-term survival estimates if OS data are immature, as is often the case in newly diagnosed MM (NDMM). We explore the use of the relationship between minimal residual disease (MRD) status and OS to reduce uncertainty of long-term survival outcomes. Objectives: To evaluate if uncertainty in long-term modeled outcomes in NDMM is reduced using a response-based partitioned survival model (PSM), whereby patients were categorized as MRD-positive or -negative, relative to a standard PSM, when OS data are immature. Methods: Standard and response-based PSMs, estimating patient life-years (LYs) over a lifetime horizon, were developed for NDMM patients treated with bortezomib, thalidomide, and dexamethasone (BTd) with or without daratumumab as induction and consolidation therapy. In the standard PSM, LYs were determined by extrapolations from individual patient data from CASSIOPEIA. In the response-based PSM, survival was dependent on MRD status at the time of the response assessment via a landmark analysis. Cox-proportional hazard ratios from external sources and CASSIOPEIA informed the relationship for OS between MRD-positive and MRD-negative, and between patients receiving BTd and daratumumab plus BTd, respectively. Uncertainty was assessed by comparing LYs and OS extrapolations from deterministic and probabilistic analyses. Results: This response-based PSM demonstrated reduced uncertainty in long-term survival outcomes compared with the standard PSM (range across extrapolations of 3.4 and 7.7 LYs for daratumumab plus BTd and BTd, respectively, vs 14.8 and 11.8 LYs for the standard PSM). It also estimated a narrower interquartile range of LYs in the probabilistic analyses for the majority of parametric extrapolations. Discussion: Alternative methods to estimate long-term survival outcomes, such as a response-based PSM, can reduce uncertainty in modeling predictions around cost-effectiveness estimates for health technology assessment bodies and payers, thereby supporting faster market access for novel therapies with immature survival data. Conclusions: Use of MRD status in a response-based PSM reduces uncertainty in modeling long-term survival in patients with NDMM and provides a greater number of clinically plausible extrapolations compared with a standard PSM.

Lifetime risk, life expectancy, loss-of-life expectancy, and lifetime healthcare expenditures for psoriasis in Taiwan: a nationwide cohort followed from 2000 to 2017.

Patients with psoriasis have a significant disease burden throughout the life course. Nevertheless, the lifetime risk and disease burden of psoriasis across the entire lifespan is rarely quantified in an easily understandable way. To estimate the cumulative incidence rate, life expectancy, loss-of-life expectancy, and lifetime healthcare expenditures for incident psoriasis. Using real-world nationwide data from the National Health Insurance Research Database of Taiwan for 2000-2017, along with the life tables of vital statistics, we estimated cumulative incidence rate, life expectancy, loss-of-life expectancy, and lifetime healthcare expenditures for those with psoriasis using a semi-parametric survival extrapolation method. A total of 217,924 new psoriasis cases were identified. The lifetime risk of psoriasis in patients aged 18-80 for both sexes decreased in Taiwan with a cumulative incidence rate of 7.93% in 2000 to 3.25% in 2017. The mean (±standard error) life expectancy after diagnosis was 27.11 (± 1.15) and 27.14 (±1.17) years for patients with moderate-to-severe psoriasis and psoriatic arthritis, respectively. Patients with moderate-to-severe psoriasis and psoriatic arthritis had a mean (±standard error) loss-of-life expectancy of 6.41 (±1.16) and 6.48 (±1.17) due to psoriasis, respectively. Male patients have higher lifetime and annual lifetime healthcare expenditures than female. Mean life expectancy, loss-of-life expectancy, and lifetime cost were relatively higher for younger patients. Among psoriatic patients, patients with moderate-to-severe psoriasis and psoriatic arthritis had substantial years of life lost, particularly for younger patients. Our results provide a reliable estimation of lifetime disease burden, and these estimates will help health authorities in cost-effectiveness assessments of public health interventions and allocation of services resources to minimize loss-of-life expectancy, and lifetime healthcare expenditures in patients with psoriasis.

A Flexible Ensemble Learning Method for Survival Extrapolation.

Survival extrapolation is an important statistical concept for estimating long-term survival from short-term clinical trial data. It is widely used in health technology assessment (HTA). Survival extrapolation is often performed by fitting one or two parametric models selected based on experience or selecting a model based on some goodness of fit statistics from a predefined collection of models. The main challenge in survival extrapolation is that the result is sensitive to model misspecification. In this study, we aim to propose a new approach that has a robust performance for survival extrapolation. We propose a new method called Ensemble Learning for Survival Extrapolation (ELSE). Instead of selecting one best model from a predefined collection, ELSE builds an ensemble model based on a collection of models from the model library. Under this framework, we construct a point estimate of the long-term survival with a weighted average of the estimates of all candidate models and derive confidence intervals using nonparametric bootstrap. With our extensive numerical simulation studies, the proposed ELSE method shows better performance than the traditionally used model selection procedure based on Akaike Information Criterion (AIC). With a real data application to the Therapeutically Applicable Research to Generate Effective Treatment Wilms Tumor project (TARGET-WT) data, the ELSE method produces better survival extrapolation results in point estimate accuracy and confidence interval coverage. We developed an ensemble learning method for survival extrapolation (ELSE) which is robust for the underline data model and has good real data performance.

MSR132 Integrating Relative Survival Framework for Survival Extrapolation in Multi-State Models—An Application to Predicting Quality-Adjusted Life Years for Chronic Myeloid Leukemia Patients

Continuous-time multi-state models provide a framework for estimating the probability of an individual transitioning from one health state to another together with time spent in each health state. In cost-utility studies, effectiveness often estimated using quality-adjusted life years (QALYs) over a lifetime horizon. Accurately estimating long-term QALYs is crucial in health technology assessment, but this usually requires extrapolating short-term follow-up data. Results are highly sensitive to how the extrapolation is performed. Survival extrapolation using a relative survival framework (i.e., extrapolating after partitioning the all-cause hazard into the expected hazard and the excess hazard) has proven to be superior to direct extrapolation of all-case survival for estimating life expectancy after a cancer diagnosis. However, this approach has not yet been implemented in multi-state models for predicting QALYs.

HTA225 A Review of External Evidence Used to Support Survival Extrapolation in Assessments of Cancer Drugs by the National Institute for Health and Care Excellence (NICE)

External evidence is often used in health technology assessment (HTA) submissions to NICE to supplement data available from the pivotal clinical trial programme when performing survival extrapolation; however, there is no published guidance (including from NICE) regarding how this should be done. This study aimed to investigate how external evidence has been used in recent appraisals of cancer drugs conducted by NICE to aid extrapolation of survival outcomes.

MSR71 Performance of AIC and BIC for the Extrapolation of Survival Data With Different Levels of Censoring

Survival extrapolation is required to estimate survival beyond the duration of a clinical trial. The selection of the survival model is partly guided by goodness-of-fit statistics, namely the Akaike information criteria (AIC) and the Bayesian information criteria (BIC). The objective of this simulation study was to assess the performance of AIC and BIC when selecting between six standard parametric survival models to extrapolate survival data with different levels of censoring.

P21 Accuracy of Overall Survival (OS) Extrapolations in French Cost-Effectiveness Reports Relative to Updated Data From Randomized Controlled Trials of Immune Checkpoint Inhibitors (ICIS)

OS extrapolations of ICIs are required to inform cost-effectiveness analyses. This is often challenging given the short follow-up of randomized controlled trials (RCTs) available for model fitting. The objective of the study was to assess the accuracy of estimates of long-term OS predicted in French cost-effectiveness assessment reports (CEARs) using longer-term RCTs data.

EE126 Cost-Effectiveness of Multi-Cancer Early Detection (MCED) Testing Using Mixture Cure Modeling (MCM)

Multi-cancer early detection (MCED) testing along with standard of care (SoC) could improve survival outcomes and lower treatment costs but increase screening costs. These estimates may depend on methods for projecting post-diagnosis survival. Mixture Cure Modeling (MCM) has been proposed for projecting survival impact with MCED tests because it includes a proportion of cured patients which is less sensitive to lead time bias. This study explores the impact of using MCM vs a simple extrapolation of survival on the cost-effectiveness of MCED testing.