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Epidemiology Modeling

Excelra can build custom epidemiology models to assess the incidence and prevalence of disease. Covariate patient characteristics can help in trial design and benchmark controlled RCTs against complex real-world clinical context.
In the era of personalized medicine, the objective is to stratify the eligible treatment population to improve efficacy and minimize adverse events. By mining EMRs we can predict the subset of patients who truly benefit from an expensive intervention, helping payers manage their cost exposure.

DISCERNING PATTERNS OF HEALTH AND DISEASE

It is imperative to understand the incidence and prevalence of a disease to forecast the trends in a population, as well as to understand the influence of risk factors on the same. Excelra can develop custom tools to predict epidemiological behaviour by modeling historical data from population level studies, real-world data sets, as well as case control studies conducted specifically to understand the disease progression and the impact of risk factors. A variety of time series models with exponential smoothing, generalized regression, autoregressive integrated moving average (ARIMA), ARIMA with Markov Chain Monte Carlo (MCMC), etc. are used to predict disease incidence. These epidemiology tools can also be used by you to align the commercial strategy to the value proposition, and create market forecast models consistent with the same.

As we move towards an era of personalized medicine, stratification of the eligible population for improved efficacy, and minimized adverse events is critical. By mining literature, as well as real-world data (RWD) sets like surveillance data, claims data, electronic medical records (EMRs), etc.,   we can predict the subset of patients who truly benefit from an intervention, helping our clients to develop strong value arguments for targeted patient populations, and helping payers manage their cost exposure. Excelra can further stratify the patient population using “-omics” data sets, to look beyond the traditional “one-size-fits-all” approach for treatment, and move towards personalized therapies.

Burden Of Disease Estimates

Typical problems we can solve for our partners

  • Stratify the eligible treatment population to improve efficacy and minimize adverse events
  • Predict the subset of patients who truly benefit from an intervention
  • Develop disease forecasting and epidemiology models to understand the natural history of disease, the impact on patients, the health system and identify the size and subgroups of target populations
  • Inform your asset plans and future development strategies