Drive better decision-making and improved outcomes in healthcare
At Parexel, we are dedicated to pioneering precision in healthcare outcomes modeling. Given the increasing complexities of diverse and dynamic patient data, traditional statistical methods can prove inadequate. Advanced parametric models (APMs) offer the opportunity to address these challenges with improved accuracy and predictive power, yielding nuanced insights on clinical outcomes and cost-effectiveness.
Implementing advanced parametric models delivers benefits throughout the drug development process:
Evidence generation
Joint survival models can increase the accuracy of real-world evidence generation by providing dynamic risk predictions based on longitudinal patient data.
Enhanced evidence generation supports ongoing clinical development, post-market surveillance, and real-world effectiveness studies.
Meaningful clinical insights
Mixture survival models have been demonstrated to uncover critical insights into patient subgroups, leading to more personalized treatment strategies and improving overall survival rates.
These insights drive the development of targeted therapies and optimize patient outcomes by tailoring treatments to the specific patient population
Faster drug approval
Advanced survival models provide more precise and reliable efficacy and safety data.
By enhancing the robustness of clinical trial analyses, these models facilitate regulatory submissions, leading to faster market access for innovative therapies.
Successful HTA evaluations
Applications of Bayesian Multi-Parameter Evidence Synthesis can improve the acceptance rate of Health Technology Assessment (HTA) submissions by incorporating diverse data sources and generating more comprehensive evidence.
Improved HTA evaluations result in better reimbursement decisions and wider patient access to new treatments.
Our solutions
Leverage the power of additional post-baseline observations to analyze treatment effectiveness in specific emergent subpopulations.
- This approach provides precise insights into treatment effect among select groups of patients that become identifiable only after randomization and facilitates optimization of treatment plans based on evolving patient responses.
Identify and analyze subgroups with distinct survival patterns based on a latent definition such as statistical cure (mixture cure model [MCM]) or other (parametric mixture model [PMM]).
- These models offer tailored insights into the clinical factors driving heterogeneous survival outcomes, potentially yielding more realistic estimates of treatment value when efficacy is largely driven by the potential for long-term survivorship.
Incorporate longitudinal measurements into survival analysis to predict individualized risk.
- This method enables dynamic risk predictions based on continuously updated patient data, accounting for treatment mechanisms of action to yield more accurate effect estimates at the population level and helping to guide the management of individual patients by clinicians.
Holistically integrate data from relevant external sources into survival models to avoid speculation in extrapolations and reduce uncertainty in estimates, by employing an appropriate Bayesian formulation (informed priors, borrowing, or evidence synthesis).
- This approach gains predictive power via supplementing the current study observations with prior data sources that represent an initial expectation, to overcome issues of data immaturity and/or sample size, thereby producing more reliable survival estimates that are based on transparent assumptions and accurately capture true decision uncertainty.
Examine patient-level dependencies of clinical endpoints to understand the relationships between different outcomes and assess the validity of a potential surrogate endpoint.These models examine the prognostic relevance of intermediate endpoints and how the timing of an intermediate event impacts the subsequent disease trajectory. They help guide patient management and allow more reliable survival predictions for slowly maturing endpoints from immature data in future studies.
- These models examine the prognostic relevance of intermediate endpoints and how the timing of an intermediate event impacts the subsequent disease trajectory, helping to guide patient management and allowing more reliable survival predictions for slowly maturing endpoints from immature data in future studies.
TSA enables to capture the true difference between treatment and compactor arm by removing the effect of crossover to other lines of subsequent treatment and hence enabling robust comparative effectiveness analysis.
- This technique results in a greater estimated relative survival benefit with active treatment versus control.
Advanced parametric models expertise from Parexel’s Advanced Analytics team
We are dedicated to delivering advanced parametric models solutions that drive better decision-making and improved outcomes in healthcare. Whether your goal is to optimize treatment strategies, assess the efficacy of new interventions, or improve resource allocation, Parexel is your trusted partner in advanced parametric models.