Time for a change? Alternative approaches to modelling in cancer value assessments

Transcription

1 Time for a change? Alternative approaches to modelling in cancer value assessments Robert Hettle (formerly PAREXEL) ISPOR 20 th Annual European Congress 07 th November 2017 An Industry perspective on the Decision Support Unit (DSU) critical review of partitioned survival modelling Guidance on the topic of partitioned survival modelling is a welcome addition to DSU and ISPOR guidance We are entering a new era of cancer treatment with rapidly accelerated development times, and growing pressure to appraise new drugs at faster rates Standardisation of methods of economic evaluation would help industry and decision makers meet accelerated appraisal timelines and ensure speedy patient access to novel drugs 2

2 Methodological uncertainty is a key issue for industry, and payers. Prior to submission Industry sets the list price of new medicines Value-based pricing depends on the results of economic analyses and payer valuation on outcomes Accurate pricing requires certainty over the modelling approach During and after submission Industry role is to provide clear evidence on the health benefits, costs and value for money of new drugs Success depends on producing a fair, balanced, and defensible appraisal of cost-effectiveness Mitigating uncertainty is an important part of this process 3 Partitioned Survival modelling Intuitive, transparent, and easily communicated approach to cost-effectiveness modelling in cancer Benefits of simplicity extends beyond submissions to HTA, and includes the communication of cost-effectiveness to non-hta stakeholders: Clinicians Patients Commissioners Uses pre-specified endpoints and often accurately predicts trial data 4

3 State Transition modelling (STM) Opens the possibility of more informed modelling of disease status 5 but increases model complexity With 3-state structure, we have 3 sets of clinical parameters versus 2 (PFS/OS) with PSM Pandora s box What are the implications of switching from PSM to STM? From PFS and OS to time to progression (death censored), time to death (progression censored) and post-progression survival Submission dossiers would increase in size and complexity requiring additional evidence not usually presented e.g. post-progression survival (PPS) Further investigation and exploration of PPS may be required Trials are not set-up to show differences in PPS or non-inferiority in PPS, complicating interpretation of evidence Additional analytics may be required to extrapolate observed PPS to progression in patients not progressed Prognostic modelling of PPS requires improved data collection at time of progression 6

4 What are the implications of switching from PSM to STM? Development of multiple models across the product lifecycle PSM is more applicable to decision problems that require network meta-analysis due to the limited reporting of TTP/PPS data Comparator landscape will inevitably evolve over a products lifecycle With uncertainty surrounding the implementation of meta-analysis to STM, a switch between modelling methods may be required over a product lifecycle Multiple models may be developed and potentially submitted to appraisals, potentially adding uncertainty to the appraisal process if results diverge Different results does not show that STM is better than PSM or that PSM is better than STM? Does the introduction of STM address the potential biases of PSM? 7 What are the implications of switching from PSM to STM? Changes in the health state structure of standard models Surrogacy between intermediary endpoints and OS are important for STM New structures/endpoints may need to be explored Historically, assessors have tended to accept PFS/OS models as adequate for decision making Lack of consistency with previous appraisals adds uncertainty, and sense of industry gaming the system 8

5 Where do we go next? DSU highlights areas of uncertainty with STM (e.g. selection bias in PPS) Joint efforts between industry and academia may help address these issues in a timely manner Multi-state survival analysis remains novel in the area of cancer modelling Development of best practice statistical guides, including guidance on judging goodness of fit would de-risk the use of this methodology Criteria for selecting Semi-Markov versus Markov models is required Decision criteria and policy recommendations for STM versus PSM E.g. based on disease characteristic (late versus early stage cancer) or maturity of evidence (OS>80% mature) Help industry make more informed decisions on the type of simulation model to use for CE analyses Provide decision-makers with confidence over the choice of method Both industry and payers need to upskill and understand the pros and cons of a switch from PSM to STM 9 Thank you

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