OBJECTIVE: While randomized control trials (RCT) are the gold standard for drug approval, there is often a lack of data directly comparing different treatment options.  An indirect comparison of the treatment effects may serve as a proxy for a head-to-head RCT, however, naively comparing treatments using published trial data without adjusting for distribution differences in patient characteristics and prognostic factors can result in misleading conclusions. A novel matched-adjusted approach to indirectly compare absolute survival estimates (median overall survival (OS) or progression free survival (PFS)) for competitive treatment options is presented. METHODS:  This proposed approach requires patient-level data for one of the treatments and summary data of patient characteristics and survival outcomes for the comparator of interest. Using this proposed method, the researcher would first decide on one or two matching variables that are prognostic for survival, and apply a program involving an extension of a common SAS 9.2 procedure, Proc Surveyselect, to select 1000 random repeated sub-samples from the original population with the same distribution of matched variables. The analysis also requires programming statements using ODS and survival analysis procedures. The median OS or PFS estimates are computed for each bootstrapped sample and a 95% confidence interval (CI) is inferred around the mean of the sampled survival estimates. These absolute survival estimates, based on the adjusted population, can then be compared to the absolute survival estimates reported in published literature of the comparator treatment. CONCLUSIONS:   In the absence of head-to-head RCT data, an adjusted indirect comparison accounts for observed differences between populations making them more comparable and results in an effect of treatment exposure on survival outcomes that is less likely due to confounders.