OBJECTIVES: The Canadian Agency for Drugs and Technologies in Health (CADTH) pan-Canadian Oncology Drug Review (pCODR) provides recommendations for oncology drug’s funding to Canadian provinces (except Quebec) and territories, based on clinical, cost-effectiveness, and patient input. Funding decision-making by provinces following a pCODR recommendation is not well understood. Our objective was to investigate the relationship between incremental cost-effectiveness ratio (ICER) and the time-to-listing for each Canadian province. METHODS: Data were extracted from the CADTH “Find a Review” database. We identified oncology drug submissions that received either a “recommend” or “recommend with (any) conditions” decision between the creation of the database (2011) and October 20, 2016. The provincial funding summary and the economic guidance document provided dates of provincial funding and the ICER, respectively. The analysis used the highest ICER proposed by the Economic Guidance Panel. We investigated the relationship between provincial time-to-list (derived from dates of recommendation and provincial listing) and the ICER through box-cox transformation and generalized linear models in SAS. RESULTS: A total of 57 (of 72) submissions had “recommended” or “recommended with (any) conditions” decision. Of these 57 positive decisions, 52 reported a non-dominant positive ICER, and 39 had sufficient information to derive the time-to-list in at least one province. Ontario, British Columbia, Alberta, Manitoba, Saskatchewan and Newfoundland had positive non-linear and Prince Edward Island had a positive linear (Additional 274 days [95%CI:74-474;p-value=0.012] per 100,000CAD increase in ICER) relationships between time-to-list and ICER – higher ICER leads to longer time-to-list. However, there was insufficient power to determine the strength of the ICER-time relationship. CONCLUSIONS: Positive pCODR recommendations are not considered similarly by provinces, and higher ICERs may lead to longer provincial funding decision-making periods. Not considering other factors (e.g., budget impact, burden of disease, patient input) in decision-making and data scarcity were limitations of this analysis.