As the CAMIS team look forward to another year of collaboration and growth, it’s also important to reflect on the achievements of the previous 12 months.

The CAMIS leadership team of Christina, Yannick and Lyn thank the community for their ongoing contributions and support to the project. There were special awards for people who really impacted on the project during 2025, helping CAMIS continue to grow and improve:

• For Technical Innovation: Michael Walshe. Thank you for adding caching to the website to make it run so much faster!
• For Shaping our Future: Logan Johnson. Thank you for challenging our core roots to be better: updates to the table of contents, help pages, running SAS and parallelisation.
• For Communications: Molly MacDiarmid. Thank you for organising our wonderful blogs this year.
• For Student of the Year: Sarah Brosens. Thank you for your amazing work on tipping point and recurrent event analysis.
• For Newcomer of the Year: Miriam Amor. Thank you for your amazing work on generalised estimating equations and for presenting at PHUSE.
• For Best Editor: Abi Terry. Thank you for correcting and expanding our previous work on Cox proportional hazards tie-handling.
• For Stretching Boundaries: Fedor Logvin. Thank you for adding great sections on propensity score matching and weighting.
• For Being an All Star Contributor: Chi-Rong Li. Thank you for completing R, SAS, EAST and Comparison pages on group sequential design for survival sample size.

CAMIS (Comparing Analysis Method Implementations in Software) is a PHUSE DVOST Working Group (WG) collaboration with PSI AIMS SIG. The CAMIS open-source repository provides essential information about the application of statistical methodology in software such as SAS, R and Python. A lack of clear specification of methods can result in an inability to reproduce the same results in different software, especially since not all options are available in all software, and existing documentation is sometimes unclear.

By documenting found differences in a repository, we reduce time-consuming efforts within the community, where multiple people are investigating the same issues. Please help us build a high-quality, easy-to-read and comprehensive repository, which will be a vital source for medical statisticians and programmers. See CAMIS - A PHUSE DVOST Working Group for the repository or how to Get Involved.