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Anal cancer is a rare disease typically treated with concurrent chemoradiotherapy. Lack of understanding of prognostic factors renders options for treatment individualisation limited. Due to the rarity of the cancer, single-centre data are rarely sufficient for robust prognostic model development. Distributed learning enables the analysis of datasets from multiple centres without exchanging sensitive individual-level patient data. This thesis aimed to determine prognostic factors for patients treated for anal cancer with modern radiotherapy by using distributed learning to analyse real- world data across an international consortium. To achieve this, a local anal cancer data warehouse was established, which includes data for 568 patients treated at Leeds Cancer Centre between 2013 and 2022. The literature was systematically reviewed to identify established prognostic factors for anal cancer outcomes after treatment with conformal radiotherapy. 19 studies were evaluated, and N stage, T stage, and sex were identified as the most prevalent clinical prognostic factors for the majority of outcomes explored. The atomCAT1 three-centre proof-of-concept study was successful in demonstrating the value of distributed learning in outcome modelling for rare cancers. This study guided the expansion of the initial collaboration into an international consortium consisting of 14 radiotherapy treatment centres. Distributed learning was implemented for collaborative prognostic model development and validation across the atomCAT consortium. In the atomCAT2 study, the distributed learning analysis of data from 1,099 patients treated across 12 centres established nodal involvement, male sex, older age, and larger primary tumour size as prognostic for poorer overall survival; male sex, higher T stage, and larger primary tumour size as prognostic for poorer locoregional control; and nodal involvement and larger primary tumour size as prognostic for poorer freedom from distant metastasis. These results may guide the design of future clinical trials in anal cancer and may ultimately aid the personalisation of treatment for future patients.