BAA - FY25C1FP4 - Genentech
Past New Requirement Updated
- Agency
-
Food and Drug Administration
Department of Health and Human Services
- Solicitation expected
- Estimated value
- $700,000 to $1,500,000
- Set-aside
- No set-aside
Forecast facts
- Anticipated award
- NAICS code
- 541714 Research and Development in Biotechnology (except Nanobiotechnology)
- Points of contact
-
- Christopher Scott christopher.scott@fda.hhs.gov
- tbd@fda.gov
Forecast details come from HHS's procurement forecast. Agencies' dates and values are estimates.
About this forecast
The Department of Health and Human Services Food and Drug Administration is forecasting a research and development contract focused on advancing statistical designs for hybrid randomized clinical trials (RCTs) in rare disease research. The primary objectives include developing causal approaches to handle missing data patterns and implementing bias correction techniques for external control integration in longitudinal clinical trial settings. The estimated solicitation release date is July 1, 2026, with an anticipated contract award by September 1, 2026.
The contract falls under NAICS code 541714 for Research and Development in Biotechnology and does not have a specific set-aside designation. The primary point of contact is Christopher Scott, who can be reached at christopher.scott@fda.hhs.gov within the Food and Drug Administration. While specific award values and detailed performance parameters are not currently available, the research aims to address critical methodological challenges in rare disease clinical trial design, potentially offering significant value to the biotechnology and medical research communities.
Forecast text
Rare diseases with longitudinal outcomes are an unmet need with only a small percentage of approved treatment. However, challenges in innovative statistical designs for hybrid RCTs (incorporation of external controls in randomized clinical trials) in the longitudinal settings remain to be tackled. As a solution to this, we propose the following aims: Aim 1: Develop causal approaches to handle different missing patterns in the longitudinal settings in the design of hybrid RCTs. Aim 2: Bias correction in designing hybrid RCTs leveraging external controls in longitudinal settings.
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