DRG Epidemiology’s coverage of renal cell carcinoma comprises epidemiological estimates of key patient populations in 45 countries worldwide. We report both the incidence and prevalence of renal cell carcinoma for each country, as well as annualized case counts projected to the national population.

In addition to forecasting incident and prevalent patient populations, we estimate the number of drug-treatment opportunities in specific lines of therapy.

DRG Epidemiology’s renal cell carcinoma forecast will answer the following questions:

  • In developing countries, what impact will economic growth and development have on the number of people diagnosed with renal cell carcinoma each year?
  • Of all people diagnosed with renal cell carcinoma, how many in each of the major mature pharmaceutical markets are drug-treated?
  • How will demographic trends, such as population aging and improving life expectancy, affect the epidemiology of  renal cell carcinoma over the forecast period?

All forecast data are available on the DRG Insights Platform in tabular format, with options to download to MS Excel. All populations are accompanied by a comprehensive description of the methods and data sources used, with hyperlinks to external sources. A summary evidence table generated as part of our systematic review of the epidemiological literature is provided for full transparency into research and methods. In addition, we provide a graph depicting the patient flow between or within different disease states for the major mature pharmaceutical markets. These patient-flow diagrams are provided at the regional level but may be requested for any specific country or forecast year.

Note: Coverage may vary by country

Table of contents

  • Epidemiology Dashboard
    • Introduction
      • Renal Cell Carcinoma Epidemiology Dashboard

Author(s): Ema Rodrigues, DSc, MPH; Nishant Kumar, MPH

Ema is an epidemiologist with expertise in forecasting incident and prevalent populations within oncology, as well as some cardiovascular indications such as venous thromboembolism. She has significant experience with statistical methods such as multivariate linear regression, conditional logistic regression, principal components analysis, mixed models, hierarchical modeling, and path analysis to account for the complex relationships among various predictors of health outcomes, particularly correlated variables. She completed her master’s and doctoral degree (MPH, ) in Environmental Health at Boston University School of Public Health, where she worked on projects investigating significant predictors of various health outcomes including central nervous system cancer, cognitive function, and birth outcomes.              

Nishant is a senior epidemiologist and head of oncology within the epidemiology team at Decision Resources Group. He also covers some CNS diseases, including Alzheimer’s disease and dementia. His key interests are developing interactive patient flows, and modelling disease progression to forecast commercially relevant drug-treatable incident and prevalent populations. Nishant also spends a lot of time collaborating with clients to help answer more specific questions through custom work and consulting projects. His qualifications include an MSc in Public Health with specialization in epidemiology and statistics from King’s College London, and a BSc in Medical Studies from the University of Birmingham.


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