A Case Study Examining the Usefulness of Cure Modelling for the Prediction of Survival Based on Data Maturity

Grant, Tim S; Burns, Darren; Kiff, Christopher; Lee, Dong Hoon

doi:10.1007/s40273-019-00867-5

Cited by 14 publications

(15 citation statements)

References 23 publications

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“…Again, this issue is not specific to mixture cure models and oncology. It has been noted that pivotal trials are unlikely to provide sufficient information to estimate cure fractions in HTA settings [ 45 ]. However, in the HTA assessment, a clinician often validates the predicted mean survival and can also then inform or validate the cure fraction in the absence of data.…”

Section: Discussionmentioning

confidence: 99%

“…2 ). For example, if we have a plateau in the KM curves, and the follow-up time is long enough, we can estimate the cure from the trial [ 45 ]. Conversely, if we know that the long-term survival at—say—25 years is known to be above a certain value, we can use putative cure values as an input in determining the parameters of the survival functions representing uncured patients.…”

Section: Methodsmentioning

confidence: 99%

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Mixture Cure Models in Oncology: A Tutorial and Practical Guidance

Felizzi

Paracha

Pöhlmann³

et al. 2021

PharmacoEconomics Open

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Novel cancer therapies are associated with survival patterns that differ from established therapies, which may include survival curves that plateau after a certain follow-up time point. A fraction of the patient population is then considered statistically cured and subject to the same mortality experience as the cancer-free general population. Mixture cure models have been developed to account for this characteristic. As compared to standard survival analysis, mixture cure models can often lead to profoundly different estimates of long-term survival, required for health economic evaluations. This tutorial is designed as a practical introduction to mixture cure models. Step-by-step instructions are provided for the entire implementation workflow, i.e., from gathering and combining data from different sources to fitting models using maximum likelihood estimation and model results interpretation. Two mixture cure models were developed to illustrate (1) an "uninformed" approach where the cure fraction is estimated from trial data and (2) an “informed” approach where the cure fraction is obtained from an external source (e.g., real-world data) used as an input to the model. These models were implemented in the statistical software R, with the freely available code on GitHub. The cure fraction can be estimated as an output from (“uninformed” approach) or used as an input to (“informed” approach) a mixture cure model. Mixture cure models suggest presumed estimates of long-term survival proportions, especially in instances where some fraction of patients is expected to be statistically cured. While this type of model may initially seem complex, it is straightforward to use and interpret. Mixture cure models have the potential to improve the accuracy of survival estimates for treatments associated with statistical cure, and the present tutorial outlines the interpretation and implementation of mixture cure models in R. This type of model will likely become more widely used in health economic analyses as novel cancer therapies enter the market. Supplementary Information The online version contains supplementary material available at 10.1007/s41669-021-00260-z.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Mixture Cure Models in Oncology: A Tutorial and Practical Guidance

Felizzi

Paracha

Pöhlmann³

et al. 2021

PharmacoEconomics Open

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“…Among the RWE publications of axi-cel and tis-cel, follow-up duration was not long enough (median follow-up duration ranged from 4 to 24 months across publications) to compare extrapolated survival data from shorter duration clinical trials to RWE observations [68][69][70][71][72][73][74][75]. Moreover, repeated validations of extrapolated techniques using real-world data should be considered as those databases are updated over time [76].…”

Section: Opportunities For Future Researchmentioning

confidence: 99%

Applying State-of-the-Art Survival Extrapolation Techniques to the Evaluation of CAR-T Therapies: Evidence from a Systematic Literature Review

et al. 2021

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Introduction: Traditional statistical techniques for extrapolating short-term survival data for anticancer therapies assume the same mortality rate for noncured and ''cured'' patients, which is appropriate for projecting survival of non-curative therapies but may lead to an underestimation of the treatment effectiveness for potentially curative therapies. Our objective was to ascertain research trends in survival extrapolation techniques used to project the survival benefits of chimeric antigen receptor T cell (CAR-T) therapies. Methods: A global systematic literature search produced a review of survival analyses of CAR-T therapies, published between January 1, 2015 and December 14, 2020, based on publications sourced from MEDLINE, scientific conferences, and health technology assessment agencies. Trends in survival extrapolation techniques used, and the rationale for selecting advanced techniques, are discussed.

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“…"Mixture-cure" functionality was therefore included as a modelling option to allow the production of outcomes where a proportion of the patient population is 'cured' and therefore experiences a probability of death equal to that of the calculated age-matched general population estimates [53,54]. The 'cured' and 'uncured' patient populations are then combined to produce curves for the overall population [55,56].…”

Section: Analysis Requirementsmentioning

confidence: 99%

R and Shiny for Cost-Effectiveness Analyses: Why and When? A Hypothetical Case Study

et al. 2020

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Introduction: Health economics models are typically built in Microsoft Excel ® owing to its wide familiarity, accessibility and perceived transparency. However, given the increasingly rapid and analytically complex decision-making needs of both the pharmaceutical industry and the field of health economics and outcomes research (HEOR), the demands of cost-effectiveness analyses may be better met by the programming language R.Objective: This case study provides an explicit comparison between Excel and R for contemporary cost-effectiveness analysis. Methods:We constructed duplicate cost-effectiveness models using Excel and R (with a user interface built using the Shiny package) to address a hypothetical case study typical of contemporary health technology assessment. Results:We compared R and Excel versions of the same model design to determine the advantages and limitations of the modelling platforms in terms of (i) analytical capability, (ii) data safety, (iii) building considerations, (iv) usability for technical and non-technical users and (v) model adaptability. Conclusions:The findings of this explicit comparison are used to produce recommendations for when R might be more suitable than Excel in contemporary cost-effectiveness analyses. We conclude that selection of appropriate modelling software needs to consider case-by-case modelling requirements, particularly (i) intended audience, (ii) complexity of analysis, (iii) nature and frequency of updates and (iv) anticipated model run-time.

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A Case Study Examining the Usefulness of Cure Modelling for the Prediction of Survival Based on Data Maturity

Cited by 14 publications

References 23 publications

Mixture Cure Models in Oncology: A Tutorial and Practical Guidance

Mixture Cure Models in Oncology: A Tutorial and Practical Guidance

Applying State-of-the-Art Survival Extrapolation Techniques to the Evaluation of CAR-T Therapies: Evidence from a Systematic Literature Review

R and Shiny for Cost-Effectiveness Analyses: Why and When? A Hypothetical Case Study

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