At the core of Cytel’s expertise is (advanced) trial data analyses which informs our models, indirect treatment comparisons, value communication tools, and reimbursement submission dossiers. Cytel is a demonstrated thought and methodology leader in improving data analytics and optimizing clinical trial data interpretation. Our experts develop health technology assessment proof protocols, analyses, and reports for trial-based analyses for the following type of outcomes:
Survival Extrapolations
Survival extrapolations are essential for long-term survival estimations which are a key element of many of our cost-effectiveness models. Cytel’s analysts have extensive experience with guideline-recommended extrapolation methods. In addition, Cytel is at the frontline of improving the acknowledged approaches with the aim to optimize survival estimations. Challenges that Cytel aims to overcome are immaturity of trial data, differentiating survival by subgroups, cure assumptions, observed change in survival in trial data, and use of external data to validate and inform extrapolations.
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PRO & Quality of Life Analyses
Trial-based estimates of Health-related quality of life (HRQoL) may be important for establishing the impact of treatment for patients and as a key input into cost-effectiveness analysis. The Cytel team has experience analyzing HRQoL data including generic measures such as the EQ-5D and SF-6D as well as disease-specific tools for both within-trial and model-based studies. With generic measures like the EQ-5D appropriate (e.g. local) value sets or mapping algorithms are applied to estimate utility values for use in decision modeling.
Surrogacy Analyses
The choice of endpoint can have a significant effect on the strategic positioning of a product, as well as the time at which it enters the market. Surrogacy analyses or endpoint validation studies are, therefore, a critical part of any discussion with regulators and local health authorities. Our team has a detailed understanding of regulator and reimbursement requirements, the challenges that arise when needing to go to market early, and experience in providing certainty in the validity of surrogate end points for producing a true clinical benefit.
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Treatment Switch/Cross Over Analyses
In oncology trials treatment switching, where patients are allowed to switch from the control to the treatment arm, is a common issue. Treatment switching can lead to bias in an intention-to-treat (ITT) analysis. For long-term survival extrapolation, controlling for treatment switching may be required to overcome this potential bias in decision models. Cytel offers the recommended techniques to help deal with treatment switching, including rank preserving structural failure time models (RPSFTM) and inverse probability of censoring weights (IPCW) methods.
Medical Resource Use
Where your trial captures medical resource use, Cytel can support the analysis of this data to support within-trial and model-based studies. Naïve and regression-based analysis of trial data is combined with unit-costing methods to estimate the short or long-term cost impact of a new intervention.
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Trial Re-Analyses for HTA Purposes
Some health authorities like the German GBA and or IQWIG require companies to conduct subgroup analyses that potentially were not conducted for the clinical study report (CSR). Cytel conducted numerous trial analyses at HTA request, considering additional subgroups. Cytel also created retrospective patient-relevant endpoints, which is important in Germany for the added benefit decision.
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