Estimating Lifetime Benefits Associated with Immuno-Oncology Therapies: Challenges and Approaches for Overall Survival Extrapolations

Ouwens, Mario; Mukhopadhyay, Pralay; Zhang, Yiduo; Huang, Min; Latimer, Nicholas; Briggs, Andrew

doi:10.1007/s40273-019-00806-4

Cited by 70 publications

(75 citation statements)

References 19 publications

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“…There have been a number of recent research publications exploring additional survival extrapolation techniques for predicting long-term outcomes associated with immunotherapy such as the use of spline models and cure fraction models [16][17][18]. These were outside the scope of the analysis; the survival extrapolation methods used in this paper were restricted to the use of standard parametric models commonly used in HTA submissions.…”

Section: Discussionmentioning

confidence: 99%

Evaluating Partitioned Survival and Markov Decision-Analytic Modeling Approaches for Use in Cost-Effectiveness Analysis: Estimating and Comparing Survival Outcomes

et al. 2019

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Objective The objective of this study was to assess long-term survival outcomes for nivolumab and everolimus in renal cell carcinoma predicted by three model structures, a partitioned survival model (PSM) and two variations of a semi-Markov model (SMM), for use in cost-effectiveness analyses. Methods Three economic model structures were developed and populated using parametric curves fitted to patient-level data from the CheckMate 025 trial. Models consisted of three health states: progression-free, progressed disease, and death. The PSM estimated state occupancy using an area under-the-curve approach from overall survival (OS) and progression-free survival (PFS) curves. The SMMs derived transition probabilities to calculate patient flow between health states. One SMM assumed that post-progression survival (PPS) was independent of PFS duration (PPS Markov); the second SMM assumed differences in PPS based on PFS duration (PPS-PFS Markov). Results All models provide a reasonable fit to the observed OS data at 2 years. For estimating cost effectiveness, however, a more relevant comparison is between estimates of OS over the modeling horizon, because this will likely impact differences in costs and quality-adjusted life-years. Estimates of the incremental mean survival benefit of nivolumab versus everolimus over 20 years were 6.6 months (PSM), 7.6 months (PPS Markov), and 7.4 months (PPS-PFS Markov), reflecting non-trivial differences of + 14% and + 11%, respectively, compared with PSM. Conclusions The evidence from this study and previous work highlights the importance of the assumptions underlying any model structure, and the need to validate assumptions regarding survival and the application of treatment effects against what is known about the characteristics of the disease.

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Section: Discussionmentioning

confidence: 99%

Evaluating Partitioned Survival and Markov Decision-Analytic Modeling Approaches for Use in Cost-Effectiveness Analysis: Estimating and Comparing Survival Outcomes

et al. 2019

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“…Empirical hazard plots (e.g. number of events per month) have been considered in a previous study as an alternative representation of the estimated hazard function (where time is considered on a continuous scale), however these plots would have limited use to inform appropriate model selection within the context of the JM200 trial due to its small sample size (n = 88) [28]. This is because there will be several periods over which the hazard of death would be estimated as zero as no events may have occurred within a given timeframe.…”

Section: Assessment Of Datamentioning

confidence: 99%

“…Previous studies which have attempted to assess the prediction accuracy of PSMs within the context of cancer immunotherapy have considered a range of techniques. Ouwens et al considered a combination of statistical goodness-of-fit and area-under-the-curve estimates [28]. Bullement et al presented a range of point estimates at specific time points relative to the maturity of data from each study [35].…”

Section: Prediction Accuracymentioning

confidence: 99%

Evaluation of survival extrapolation in immuno-oncology using multiple pre-planned data cuts: learnings to aid in model selection

Bullement¹,

Willis

Amin

et al. 2020

BMC Med Res Methodol

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Background: Due to limited duration of follow up in clinical trials of cancer treatments, estimates of lifetime survival benefits are typically derived using statistical extrapolation methods. To justify the method used, a range of approaches have been proposed including statistical goodness-of-fit tests and comparing estimates against a previous data cut (i.e. interim data collected). In this study, we extend these approaches by presenting a range of extrapolations fitted to four pre-planned data cuts from the JAVELIN Merkel 200 (JM200) trial. By comparing different estimates of survival and goodness-of-fit as JM200 data mature, we undertook an iterative process of fitting and re-fitting survival models to retrospectively identify early indications of likely long-term survival. Methods: Standard and spline-based parametric models were fitted to overall survival data from each JM200 data cut. Goodness-of-fit was determined using an assessment of the estimated hazard function, information theorybased methods and objective comparisons of estimation accuracy. Best-fitting extrapolations were compared to establish which one provided the most accurate estimation, and how statistical goodness-of-fit differed. Results: Spline-based models provided the closest fit to the final JM200 data cut, though all extrapolation methods based on the earliest data cut underestimated the 'true' long-term survival (difference in restricted mean survival time [RMST] at 36 months: − 1.1 to − 0.5 months). Goodness-of-fit scores illustrated that an increasingly flexible model was favored as data matured. Given an early data cut, a more flexible model better aligned with clinical expectations could be reasonably justified using a range of metrics, including RMST and goodness-of-fit scores (which were typically within a 2-point range of the statistically 'best-fitting' model). Conclusions: Survival estimates from the spline-based models are more aligned with clinical expectation and provided a better fit to the JM200 data, despite not exhibiting the definitively 'best' statistical goodness-of-fit. Longer-term data are required to further validate extrapolations, though this study illustrates the importance of clinical plausibility when selecting the most appropriate model. In addition, hazard-based plots and goodness-of-fit tests from multiple data cuts present useful approaches to identify when a more flexible model may be advantageous.

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“…At 5 years, extrapolations with optimal goodness of fit for OS of 49% and 26% and PFS of 36% and 8% are comparable with the reported data. 18 This is supported by Ouwens et al 22 who conclude that standard parametric methods underestimate long-term I-O OS data, where more research into mature OS data is needed to improve our understanding of the realism of OS projections. Here, it is important to recognise that, although assumptions were applied to transition from the conventional 3-state to the proposed 5-state PSM, it is the granularity in the data as seen in the BOR that can be useful in addition to the OS and PFS predictions, to assess if clinical benefit has been captured by respective modelling frameworks submitted as part of economic evaluations.…”

Section: Discussionmentioning

confidence: 92%

<p>Economic Evaluation of Single versus Combination Immuno-Oncology Therapies: Application of a Novel Modelling Approach in Metastatic Melanoma</p>

Gibson¹,

Begum²,

Koblbauer³

et al. 2020

CEOR

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Background: Existing economic model frameworks may not adequately capture the atypical treatment response patterns in immuno-oncology (I-O) compared with conventional therapies and thus may fail to represent the full clinical value associated with disease dynamics and improved survival. Objective: A cost-effectiveness analysis (CEA) of the I-O Regimen (nivolumab/ipilimumab) versus ipilimumab alone in advanced melanoma was carried out by applying a 5-state partitioned survival model (PSM) as a case study, to explore the I-O treatment response and clinical outcomes. The findings were compared with those of a conventional 3-state PSM. Materials and Methods: The case study extends the conventional 3-state PSM, by separating the pre-progression state into non-responders and responders, and the post-progression state into normal and I-O progression to account for delayed treatment effects preceding clinical response. Model states were populated using patient-level data (where possible), mapping from the best overall response (BOR), and survival analysis with flexible and traditional parametric methods. Survival functions were applied to progression-free survival (PFS) and overall survival (OS) endpoints across treatment arms using the 4-year follow-up data (data available at the time of the research; since then 5-year follow-up data have been published) from the CheckMate 067 trial. Information on BOR was used as a means of differentiating the I-O treatment response in addition to the outcomes of progression-free and progressed disease. A UK National Health Service and personal social services (NHS/PSS) perspective over a lifetime horizon was used with outcomes discounted at 3.5% annually. Results: The 5-state PSM generated an increase in quality adjusted life years (QALYs) in both treatment arms and gave a more granular description of patients' health profiles compared with the traditional 3-state PSM. The incremental QALY increased by 13% (from 2.62 to 2.95 QALYs) and the incremental cost decreased by 12% (£29,125 to £25,678) with the 5-state model. In both models, the Regimen had an incremental cost-effectiveness ratio (ICER) relative to ipilimumab alone within the lower bound of the National Institute for Health and Care Excellence (NICE) reference range (£20,000 per QALY gained). Conclusion: A 5-state economic model, incorporating relevant I-O health states, can be more informative to gain insight into treatment response and progression differences that are not commonly captured in existing economic models. Clinical trial endpoints, including those relating to treatment response, which are not directly reported in ongoing I-O trials, can be mapped on to the proposed modelled health states (although assumptions are required to do so). Improvements in reporting treatment response in future I-O clinical trials could help to further validate and improve the proposed model framework.

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Estimating Lifetime Benefits Associated with Immuno-Oncology Therapies: Challenges and Approaches for Overall Survival Extrapolations

Cited by 70 publications

References 19 publications

Evaluating Partitioned Survival and Markov Decision-Analytic Modeling Approaches for Use in Cost-Effectiveness Analysis: Estimating and Comparing Survival Outcomes

Evaluating Partitioned Survival and Markov Decision-Analytic Modeling Approaches for Use in Cost-Effectiveness Analysis: Estimating and Comparing Survival Outcomes

Evaluation of survival extrapolation in immuno-oncology using multiple pre-planned data cuts: learnings to aid in model selection

<p>Economic Evaluation of Single versus Combination Immuno-Oncology Therapies: Application of a Novel Modelling Approach in Metastatic Melanoma</p>

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