Decision Analytics and Modeling
The gap in real-world evidence has emerged as a critical challenge to drug development and market access strategy. Complex decisions have to be taken from early stages to launch, reimbursement and pricing, that should take into account the real world value of the medicines and health products. A series of scenarios must be explored in each situation, and defended to decision makers and payers.
Through a suite of dedicated and proven methodologies, our Real-World Analytics team can leverage and integrate all the relevant information available at any stage of development and market access to estimate or predict effectiveness in a given context and to inform strategic decisions for drug evaluation and evidence generation.
LASER ANALYTICA uses the best of modeling, simulation, Bayesian statistics and epidemiological sciences to build effectiveness, relative effectiveness and real-life benefit/risk arguments specific to each country. All predictive models are used.
- Bridging-to-Real World Studies™: this proprietary study concept combines advanced predictive modeling with targeted real-world data-collection.
- Strategic Development Analytics: early decision modeling & optimal clinical program design, surrogate endpoint validation & epidemiological forecasting
- Economic Optimization: therapy sequence modeling & value-based pricing optimization
- Access Data Platforms: clinical and observational data are integrated into a platform to optimize evidence generation and real world impact assessment
- Quantitative Evidence Synthesis: These powerful methods provide very useful information to support HTA submissions, value dossiers, as well as internal decision-making for drug development.
Bridging to Real World
Bridging studies combine advanced predictive and integrative modeling with targeted clinical and real-life data analyses. Prospective or retrospective data collection can be designed in an optimal way leveraging all evidence available at present. Such studies can be used to bridge from efficacy to effectiveness, from country to country, and from one population to another. Bridging models allow individual patient simulations over time that mimic the dynamics of health outcomes, prescriptions and adherence over time. They rely on a structured analysis and quantification of the factors interacting with efficacy in real-life and their interaction with each other and over time. These effectiveness drivers include adherence, prescription patterns, treatment pathways, and patient characteristics. Data from electronic healthcare databases, our PGRx information system, disease registries or real-world studies in other countries are summarised into Access Data Platforms.
An unrivalled option to anticipate drug real-world value
The design and conduct of a bridging study requires advanced pharmacoepidemiological and statistical capabilities such as Bayesian hierarchical modeling, copula models, stochastic time-dependent processes and continuous Markov chains.
Bridging effectiveness models can be complemented by a cost layer to construct a real-world cost-effectiveness model providing more accurate outputs than efficacy-based models. These models can also be used to support risk-sharing agreements and value-based pricing.
Strategic development analytics
LASER ANALYTICA offers superior analytics to evaluate and document the anticipated real-life benefit, risk and cost of new interventions and to support internal decisions (market access strategy for products, port-folio prioritization, trial design), evidence building for a particular product and applications to authorities for approval and reimbursement.
We use a variety of model types as appropriate:Markov/cohort,decision analytic,Bayesian,patient-level models, discrete-event/Monte Carlo simulations, and typically include deterministic and probabilistic sensitivity analyses. Our Modeling & Simulation methods are state of the art.
Our services include:
Early decision analytic models
LASER ANALYTICA designs customized disease models to explore scenarios and recast a future product’s value into evolving therapeutic landscapes. Our models help position products but also identify uncertainties and gaps in evidence when there is still time to fill them. Models are built on structured clinical and observational data, clinician consultations, appropriate early mixed treatment comparisons, etc., and typically include comparisons of clinical outcomes, market share or cost-effectiveness/consequences/utility for different scenarios.
Strategic optimal clinical program design
We provide forecasting support to evaluate the probability of success for our clients’ individual trials or clinical program under different scenarios. Our simulations combine:
- Clinical insight, including forecasting of clinical endpoints and real-world outcomes from biomarkers and surrogates
- Sound and transparent statistical design, including variability and uncertainty estimation
We strive to help decisionmakers gain confidence in their choice of target population, sample size, choice of primary and secondary endpoints for optimal trial results.
Epidemiology forecasting models
These models are a subtype of early decision analytic models that consider the macro-level evolution of a disease and associated consequences to best anticipate changes due to evolution in therapy standards and populations for a particular disease and setting.
Surrogates of clinically relevant endpoints are increasingly often relied upon to assess benefit-risk and drive decisions on approval and reimbursement of new therapies.
Surrogate endpoint validation
We propose state-of-the-art surrogate validation to justify their use instead of clinical endpoints (in accordance with the most recent IQWiG or NICE guidelines)
Economic optimization models
Therapy sequence models
- Total clinical benefit, such as progression-free survival and overall survival,
- Total cost, for example including drug cost and cost of treating adverse events,
- Patient distributions across therapies at each treatment line, and
- Required volume of each drug under several sequencing scenarios
Our models are transparent and make it easy to assess increasingly complex therapeutic environments with multiple possible treatment sequences.
Value-based pricing optimization
We provide a transparent way of assessing and weighing risks of non-reimbursement while maximizing earnings to back up negotiations towards garnering or maintaining therapy reimbursement.
Our price models are built on relevant historical data and guide the choice of the optimal price/population/funding conditions for the given therapy, in reducing uncertainty and narrowing down the range of options to choose from.
Our simple graphical representation of pricing implications makes it easy to visualize options.
See also Economic Modelling to support
Modeling & Simulation Methods
Thoughtful definition of the modeling framework and scope
Transparent model building process
Advanced and broad modeling skills
Access Data Platforms
The analysis of these databases benefits from the advanced pharmacoepidemiological and statistical expertise of our real-world research teams.