The RTI University Collaboration Office seeks a prospect for the HEDM Dissertation Placement Internship within the Health Solutions unit.
As a business unit of RTI International, RTI-HS provides global research and consulting services to pharmaceutical and healthcare companies in health economics, patient-centred outcomes research, market access, epidemiology, and clinical research services. As a non-profit, independent organization, our 330-person multidisciplinary team brings a balanced approach of scientific rigor and industry context to projects
Health technology assessment (HTA) agencies such as the National Institute for Health and Care Excellence (NICE) need to make recommendations based on cost-effectiveness analyses typically over a lifetime horizon, therefore requiring evidence of long-term impacts on overall survival (OS). However, regulators are increasingly granting accelerated or conditional approvals for oncology therapies based on immature data on OS, meaning long-term survival benefits of oncology therapies may not be available at the time of HTA submission. Typical approaches to predicting and extrapolating OS include the use of traditional parametric or more complex survival models, often alongside external data from the same disease area. Guidelines have noted that very immature survival data is likely to lead to inappropriate extrapolation, particularly where external data is not available to assess the plausibility of extrapolations (Rutherford et al., 2020). Surrogate endpoint analysis offers an alternative approach to extrapolating effects on OS. Although OS data in a trial may be immature, treatment effects on intermediate outcomes (surrogates), such as response rates, may be available. Where strong relationships between treatment effects on surrogates and OS can be estimated from other sources, treatment effects on surrogates can be used to predict longer-term treatment effects on OS. Recent guidelines developed by the NICE Decision Support Unit have outlined the gold-standard approaches to identifying and validating surrogates based on multivariate meta-analysis (Bujkiewicz et al., 2019; Welton et al., 2020). However, little has been discussed regarding the timepoints at which effects of surrogates are reported. The existence of multiple publications for a given trial often provide effects on response rates (and OS) at multiple stages of follow-up. Analyses of surrogate relationships primarily select a single effect for each trial, and typically don’t report the timepoint used. It is also common for studies to select effects on the surrogate and OS from the same timepoint. This is somewhat inconsistent with how surrogate relationships are applied in practice, where treatment effects on surrogates at an earlier timepoint will be used to predict treatment effects on OS at a later follow-up. Using response rates as an example, this project will explore whether the strength of surrogate relationships varies by difference in follow-up used to estimate treatment effects on surrogates and OS. The study will be conducted in an oncology setting, with the specific cancer selected following discussions with the student. The project will include a systematic literature review (SLR) component to identify trials and extract treatment effects on response rates and OS. This will be followed in a second step by an analysis component that will use meta-analysis methods recommended in NICE guidelines to estimate surrogate relationships. In a final step, a simple model will be built to explore the impact of results from the second step on conclusions regarding cost-effectiveness
RTI's Internship Program emphasizes experiential and mentored learning experiences, providing students and recent degree recipients an opportunity to apply their academic knowledge and skills in a meaningful and practical way. We are committed to developing the future workforce by encouraging interns to explore various career paths in a nonprofit research institute setting that values diversity and inclusion. Through this program, interns will be exposed to RTI's open and educational culture; support RTI's mission to improve the human condition; and build upon their career goals.
This virtual paid with academic credit internship will take place from June 2022 through September 2022. The internship requires a minimum commitment of 21 hours per week. Hours are flexible during business hours, Monday through Friday.
Required QualificationsCurrently enrolled student pursuing an MSc in Health Economics and Decision Modelling (HEDM) from the School of Health and Related Research (ScHARR, the University of Sheffield). Experience in Excel as well as experience in the software R. Demonstrated knowledge of or familiarity with performing quantitative analysis Excellent oral and written communication skills, as well as strong interpersonal skills Proficient with Microsoft Office Suite, including Word, Excel, PowerPoint, and Outlook