Large randomized clinical trials testing the impact of subject-, disease- and transplant-related co-variates on outcomes amongst recipients of haematopoietic cell transplants are uncommon. For example, who is the best donor, which is the best pretransplant conditioning regimen or the best regimen to prevent or treat acute and/or chronic graft-versus-host disease. To answer these questions we often rely on analyses of data from large observational datasets such as those of the Center for International Blood and Marrow Transplant Research (CIBMTR) and the European Society for Blood and Marrow Transplantation (EBMT). Such analyses have proved extremely important in advancing the field. However, in contrast to randomized trials, we cannot be certain potentially important prognostic or predictive co-variates are balanced between cohorts selected for comparison from an observational dataset, a limitation which can lead to incorrect conclusions. In the typescript which follows the authours describe a method to adjust for known imbalances in co-variates and get a closer approximation of the truth. They give two examples, the impact of a new pretransplant conditioning regimen on disease-free survival (DFS) in subjects with Ewing sarcoma and the impact of donor-type on treatment-related mortality (TRM) and leukaemia relapse in subjects with acute leukaemia. Direct adjusted survival and cumulative incidence function (CIF) analyses are an important step forward. These analyses can be done using available statistical packages and we encourage readers to use them rather than reporting unadjusted analyses. Finally, we must emphasize direct adjustment can only be done for know prognostic or predictive co-variates, not unknown co-variates. Unknown co-variates will be balanced in randomized trials which is why we do them. So direct adjustment is an important step forward but not a perfect substitute for randomized trials. But any step forward is important. To quote Laozi: (A journey of a thousand miles begins with a single step).