OBJECTIVES: Administrative databases are often used to investigate patterns of health care resource use and expenditure over time for particular conditions in order to project future costs and to evaluate the cost-effectiveness of interventions. Duration of the period of observation typically varies across individuals due to differences in enrollment/disenrollment dates, timing of the index event, and the database time span. Projecting expenditure on an annual or per member per month basis requires standardizing these periods of observation. We evaluate bias introduced by a simple method for annualizing utilization and cost measures. METHODS: We investigated resource use and costs surrounding an index event; hospital admission with a primary diagnosis of heart failure. For each patient, claims data were available for a pre-event and post-event period ranging from 6 to 24 months. We standardized periods to one year pre- and post-event by first tabulating counts and costs for the actual period observed. We then multiplied these values by the ratio of 365 days to actual days in the period such that values for periods shorter than 365 days were inflated and longer than 365 days deflated. To determine whether this adjustment biased the magnitude of annualized values, we estimated a regression model with annualized cost and adjustment ratio as dependent and independent variables respectively. RESULTS: For the pre-event period, there was no significant association between annualized cost and adjustment factor (p=0.72, R2=0.00014, p=0.012) indicating absence of bias. While bias was observed in the post-event adjustment factor (p=0.005), the low model explanatory power (R2=0.00869) and lack of correlation (Pearson correlation coefficient 0.093) suggest minimal impact on the magnitude of annualized estimates. CONCLUSION: We find that a simple approach to standardization was reasonably robust. We compare advantages and disadvantages with more complex regression-based methods.