Comparison of Parametric Survival Extrapolation Approaches Incorporating General Population Mortality for Adequate Health Technology Assessment of New Oncology Drugs

Oostrum, I. van; Ouwens, Mario; Remiro‐Azócar, Antonio; Baio, Gianluca; Postma, Maarten J.; Buskens, Erik; Heeg, Bart

doi:10.1016/j.jval.2021.03.008

Cited by 25 publications

(30 citation statements)

References 33 publications

(57 reference statements)

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“…Advanced methods for formally integrating external data—either observed or elicited via clinical expert—have recently been explored, and this topic is an area of ongoing research and development. 6 , 25 – 29 …”

Section: Discussionmentioning

confidence: 99%

“…Advanced methods for formally integrating external data-either observed or elicited via clinical experthave recently been explored, and this topic is an area of ongoing research and development. 6,[25][26][27][28][29] As part of Technical Support Document 21, a simulation exercise was conducted to evaluate the performance of a subset of the flexible modeling techniques under different scenarios representing various ''true'' complex hazard shapes (based on Weibull distributions). One such scenario assumed that there was a signal indicating a cure fraction exists, and the presented survival and hazard plots most closely aligned with the shapes seen in CheckMate 067.…”

Section: Discussionmentioning

confidence: 99%

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Heterogeneity in Survival with Immune Checkpoint Inhibitors and Its Implications for Survival Extrapolations: A Case Study in Advanced Melanoma

Paly

Kurt

Zhang

et al. 2022

MDM Policy & Practice

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Background Survival heterogeneity and limited trial follow-up present challenges for estimating lifetime benefits of oncology therapies. This study used CheckMate 067 (NCT01844505) extended follow-up data to assess the predictive accuracy of standard parametric and flexible models in estimating the long-term overall survival benefit of nivolumab plus ipilimumab (an immune checkpoint inhibitor combination) in advanced melanoma. Methods Six sets of survival models (standard parametric, piecewise, cubic spline, mixture cure, parametric mixture, and landmark response models) were independently fitted to overall survival data for treatments in CheckMate 067 (nivolumab plus ipilimumab, nivolumab, and ipilimumab) using successive data cuts (28, 40, 52, and 60 mo). Standard parametric models allow survival extrapolation in the absence of a complex hazard. Piecewise and cubic spline models allow additional flexibility in fitting the hazard function. Mixture cure, parametric mixture, and landmark response models provide flexibility by explicitly incorporating survival heterogeneity. Sixty-month follow-up data, external ipilimumab data, and clinical expert opinion were used to evaluate model estimation accuracy. Lifetime survival projections were compared using a 5% discount rate. Results Standard parametric, piecewise, and cubic spline models underestimated overall survival at 60 mo for the 28-mo data cut. Compared with other models, mixture cure, parametric mixture, and landmark response models provided more accurate long-term overall survival estimates versus external data, higher mean survival benefit over 20 y for the 28-mo data cut, and more consistent 20-y mean overall survival estimates across data cuts. Conclusion This case study demonstrates that survival models explicitly incorporating survival heterogeneity showed greater accuracy for early data cuts than standard parametric models did, consistent with similar immune checkpoint inhibitor survival validation studies in advanced melanoma. Research is required to assess generalizability to other tumors and disease stages. Highlights Given that short clinical trial follow-up periods and survival heterogeneity introduce uncertainty in the health technology assessment of oncology therapies, this study evaluated the suitability of conventional parametric survival modeling approaches as compared with more flexible models in the context of immune checkpoint inhibitors that have the potential to provide lasting survival beneﬁts. This study used extended follow-up data from the phase III CheckMate 067 trial (NCT01844505) to assess the predictive accuracy of standard parametric models in comparison with more flexible methods for estimating the long-term survival benefit of the immune checkpoint inhibitor combination of nivolumab plus ipilimumab in advanced melanoma. Mixture cure, parametric mixture, and landmark response models provided more accurate estimates of long-term overall survival versus external data than other models tested. In this case study with immune checkpoint inhibitor therapies in advanced melanoma, extrapolation models that explicitly incorporate differences in cancer survival between observed or latent subgroups showed greater accuracy with both early and later data cuts than other approaches did.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Heterogeneity in Survival with Immune Checkpoint Inhibitors and Its Implications for Survival Extrapolations: A Case Study in Advanced Melanoma

Paly

Kurt

Zhang

et al. 2022

MDM Policy & Practice

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“…Recently, alternative methods for incorporating GPM into survival extrapolations, such as incorporating GPM hazards in the log-link function, have come to light, which might influence survival estimates. 17,41 As such, exploring alternative methods to incorporate GPM into this type of analysis requires further investigation. A second limitation of this analysis was that GPM was not sampled in the PSA.…”

Section: Strengths and Limitationsmentioning

confidence: 99%

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

Hest¹,

Morten²,

Stubbs³

et al. 2023

JHEOR

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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.

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“…Similarly, decision analytical models, which provide evidence to guide decision making by utilizing mathematical techniques to synthesize data comparing expected costs and consequences of potential decisions ( 26 ), can be used to evaluate long-term outcomes as well as economic impacts ( 27 ). Survival extrapolation that includes general population mortality has been recently utilized ( 28 ) for evaluating oncology treatments in companion animals. While some of the trends observed in human and veterinary healthcare overlap, a disconnect in methodologies remains, and thus, a tremendous benefit could be gained by translating and applying these methods to current issues in animal health.…”

Section: Introductionmentioning

confidence: 99%

The application, value, and impact of outcomes research in animal health and veterinary medicine

et al. 2022

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Outcomes research is a relatively recent field of study in animal health and veterinary medicine despite being well-established in human medicine. As the field of animal health is broad-ranging in terms of animal species, objectives, research methodologies, design, analysis, values, and outcomes, there is inherent versatility in the application and impact of the discipline of outcomes research to a variety of stakeholders. The major themes of outcomes relevant to the animal health industry have been distilled down to include, but are not limited to, health, production, economics, and marketing. An outcomes research approach considers an element of value along with an outcome of interest, setting it apart from traditional research approaches. Elements of value are determined by the stakeholders' use of products and/or services that meet or exceed functional, emotional, life-changing, and/or societal needs. Stakeholder perception of value depends on many factors such as the purpose of the animal (e.g., companion vs. food production) and the stakeholder's role (e.g., veterinarian, client, pet-owner, producer, consumer, government official, industry representative, policy holder). Key areas of application of outcomes research principles include comparative medicine, veterinary product development, and post-licensure evaluation of veterinary pharmaceuticals and/or biologics. Topics currently trending in human healthcare outcomes research, such as drug pricing, precision medicine, or the use of real-world evidence, offer novel and interesting perspectives for addressing themes common to the animal health sector. An approach that evaluates the benefits of practices and interventions to veterinary patients and society while maximizing outcomes is paramount to combating many current and future scientific challenges where feeding the world, caring for our aging companion animals, and implementing novel technologies in companion animal medicine and in production animal agriculture are at the forefront of our industry goals.

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Comparison of Parametric Survival Extrapolation Approaches Incorporating General Population Mortality for Adequate Health Technology Assessment of New Oncology Drugs

Cited by 25 publications

References 33 publications

Heterogeneity in Survival with Immune Checkpoint Inhibitors and Its Implications for Survival Extrapolations: A Case Study in Advanced Melanoma

Heterogeneity in Survival with Immune Checkpoint Inhibitors and Its Implications for Survival Extrapolations: A Case Study in Advanced Melanoma

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

The application, value, and impact of outcomes research in animal health and veterinary medicine

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