Healthcare-associated infections (HAIs) unquestionably have substantial effects on morbidity and mortality. However, quantifying the exact economic burden attributable to HAIs still remains a challenging issue. Inaccurate estimations may arise from two major sources of bias. First, factors other than infection may affect patients' length of stay (LOS) and healthcare utilization. Second, HAI is a time-varying exposure, as the infection can impact on LOS and costs only after the infection has started. The most frequent mistake in previously published evidence is the introduction of time-dependent information as time-fixed, on the assumption that the impact of such exposure on the outcome was already present on admission. Longitudinal and multistate models avoid time-dependent bias and address the time-dependent complexity of the data. Appropriate statistical methods are important in analysis of excess costs and LOS associated with HAI, because informed decisions and policy developments may depend on them.