Faculty Collaboration Database

Direct adjusted survival and cumulative incidence curves for observational studies. Bone Marrow Transplant 2020 Mar;55(3):538-543

Date

05/19/2019

Pubmed ID

31101889

Pubmed Central ID

PMC7306148

DOI

10.1038/s41409-019-0552-y

Scopus ID

2-s2.0-85066013297 (requires institutional sign-in at Scopus site)   17 Citations

Abstract

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).

Author List

Hu ZH, Peter Gale R, Zhang MJ

Author

Mei-Jie Zhang PhD Professor in the Institute for Health and Equity department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Disease-Free Survival
Graft vs Host Disease
Hematopoietic Stem Cell Transplantation
Humans
Incidence
Neoplasm Recurrence, Local
Observational Studies as Topic
Transplantation Conditioning