In cost-effectiveness analysis, interest could lie foremost in the incremental cost-effectiveness ratio (ICER), which is the ratio of the incremental cost to the incremental benefit of two competing interventions. The average cost-effectiveness ratio (ACER) is the ratio of the cost to benefit of an intervention without reference to a comparator. A vast literature is available for statistical inference of the ICERs, but limited methods have been developed for the ACERs, particularly in the presence of censoring. Censoring is a common feature in prospective studies, and valid analyses should properly adjust for censoring in cost as well as in effectiveness. In this article, we propose statistical methods for constructing a confidence interval for the ACER from censored data. Different methods-Fieller, Taylor, bootstrap-are proposed, and through simulation studies and data analysis, we address the performance characteristics of these methods.