Analysis of survival outcomes in haematopoietic cell transplant studies: Pitfalls and solutions
Series Editor Introduction The final article in our Statistics Series by de Wreede and colleagues deals with the important issue of survival analyses in general and in recipients of haematopoietic cell transplants specifically. At first glance analyzing survival should be simple. The endpoint is cle...
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| Published in | Bone marrow transplantation (Basingstoke) Vol. 57; no. 9; pp. 1428 - 1434 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
01.09.2022
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0268-3369 1476-5365 1476-5365 |
| DOI | 10.1038/s41409-022-01740-4 |
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| Abstract | Series Editor Introduction
The final article in our Statistics Series by de Wreede and colleagues deals with the important issue of survival analyses in general and in recipients of haematopoietic cell transplants specifically. At first glance analyzing survival should be simple. The endpoint is clear with rare exception, the subject is either alive or dead. Compare this to other less well defined transplant-related outcomes such as who has acute graft-
versus
-host disease (G
v
HD) and of what grade or what is the cause of interstitial pneumonia. There is also the complexity of composite endpoints when one analyzes outcomes such as event-free (EFS) or relapse-free survival (RFS). Here you’re either alive or dead. Period. Alas, as it turns out things are not so simple. As the authours point out:
it takes time to observe time
. It is almost never possible to wait long enough for everyone in a study to die. (Some people who are cured by a transplant will outlive their physician and statistician.) Other subjects may not be followed until the end of the study, lost to follow-up or withdraw consent to participate. Often these are non-random events, muddy the water and make what seems a simple analysis of survival not so. Fortunately, de Wreede and colleagues discuss the issues of
informative and non-informative censoring
and time-dependent co-variates. And there are other nasty complexities such non-proportional hazards of death say when initially there is a survival disadvantage to transplants from transplant-related mortality followed in 1–2 years by a survival benefit. They emphasize the danger of considering only Hazard Ratio in this setting. Lastly, the authours discuss how to compare interventions such as conventional therapy
versus
a haematopoietic cell transplant when the endpoint of interest is survival. We think this article will be of considerable interest to readers of BONE MARROW TRANSPLANTATION and suggest you study it carefully. Survival analyses, seemingly simple, are a potential minefield. You don’t want to step on one. This article and the entire Statistics Series are available online at
https://www.nature.com/collections/ejhigdbeeh
.
Robert Peter Gale MD, PhD & Mei-Jie Zhang PhD.
The most important outcome of many studies of haematopoietic cell transplants is survival. The statistical field that deals with such outcomes is survival analysis. Methods developed in this field are also applicable to other outcomes where the occurrence and timing are important. Analysis of such
time-to-event outcomes
has special challenges because
it takes time to observe time
. The most important condition for unbiased estimation of a survival curve—non-informative censoring—is discussed along with methods to account for competing risks, a situation where multiple, mutually-exclusive endpoints are of interest. Techniques to compare survival outcomes between groups are reviewed, including the instance where it is unknown at baseline to which group a subject will belong later during follow-up (time-dependent covariates).
Bell ringing coffin used in England in the Middle Ages in case of accidental burial. Popular with people with taphophobia. More information if you are interested at:
https://www.vox.com/2015/7/31/9075011/buried-alive-safety-coffins
. |
|---|---|
| AbstractList | The final article in our Statistics Series by de Wreede and colleagues deals with the important issue of survival analyses in general and in recipients of haematopoietic cell transplants specifically. At first glance analyzing survival should be simple. The endpoint is clear with rare exception, the subject is either alive or dead. Compare this to other less well defined transplant-related outcomes such as who has acute graft-versus-host disease (GvHD) and of what grade or what is the cause of interstitial pneumonia. There is also the complexity of composite endpoints when one analyzes outcomes such as event-free (EFS) or relapse-free survival (RFS). Here you're either alive or dead.SERIES EDITOR INTRODUCTIONThe final article in our Statistics Series by de Wreede and colleagues deals with the important issue of survival analyses in general and in recipients of haematopoietic cell transplants specifically. At first glance analyzing survival should be simple. The endpoint is clear with rare exception, the subject is either alive or dead. Compare this to other less well defined transplant-related outcomes such as who has acute graft-versus-host disease (GvHD) and of what grade or what is the cause of interstitial pneumonia. There is also the complexity of composite endpoints when one analyzes outcomes such as event-free (EFS) or relapse-free survival (RFS). Here you're either alive or dead.Alas, as it turns out things are not so simple. As the authours point out: it takes time to observe time. It is almost never possible to wait long enough for everyone in a study to die. (Some people who are cured by a transplant will outlive their physician and statistician.) Other subjects may not be followed until the end of the study, lost to follow-up or withdraw consent to participate. Often these are non-random events, muddy the water and make what seems a simple analysis of survival not so. Fortunately, de Wreede and colleagues discuss the issues of informative and non-informative censoring and time-dependent co-variates. And there are other nasty complexities such non-proportional hazards of death say when initially there is a survival disadvantage to transplants from transplant-related mortality followed in 1-2 years by a survival benefit. They emphasize the danger of considering only Hazard Ratio in this setting. Lastly, the authours discuss how to compare interventions such as conventional therapy versus a haematopoietic cell transplant when the endpoint of interest is survival. We think this article will be of considerable interest to readers of BONE MARROW TRANSPLANTATION and suggest you study it carefully. Survival analyses, seemingly simple, are a potential minefield. You don't want to step on one. This article and the entire Statistics Series are available online at https://www.nature.com/collections/ejhigdbeeh . Robert Peter Gale MD, PhD & Mei-Jie Zhang PhD. The most important outcome of many studies of haematopoietic cell transplants is survival. The statistical field that deals with such outcomes is survival analysis. Methods developed in this field are also applicable to other outcomes where the occurrence and timing are important. Analysis of such time-to-event outcomes has special challenges because it takes time to observe time. The most important condition for unbiased estimation of a survival curve-non-informative censoring-is discussed along with methods to account for competing risks, a situation where multiple, mutually-exclusive endpoints are of interest. Techniques to compare survival outcomes between groups are reviewed, including the instance where it is unknown at baseline to which group a subject will belong later during follow-up (time-dependent covariates).PERIODAlas, as it turns out things are not so simple. As the authours point out: it takes time to observe time. It is almost never possible to wait long enough for everyone in a study to die. (Some people who are cured by a transplant will outlive their physician and statistician.) Other subjects may not be followed until the end of the study, lost to follow-up or withdraw consent to participate. Often these are non-random events, muddy the water and make what seems a simple analysis of survival not so. Fortunately, de Wreede and colleagues discuss the issues of informative and non-informative censoring and time-dependent co-variates. And there are other nasty complexities such non-proportional hazards of death say when initially there is a survival disadvantage to transplants from transplant-related mortality followed in 1-2 years by a survival benefit. They emphasize the danger of considering only Hazard Ratio in this setting. Lastly, the authours discuss how to compare interventions such as conventional therapy versus a haematopoietic cell transplant when the endpoint of interest is survival. We think this article will be of considerable interest to readers of BONE MARROW TRANSPLANTATION and suggest you study it carefully. Survival analyses, seemingly simple, are a potential minefield. You don't want to step on one. This article and the entire Statistics Series are available online at https://www.nature.com/collections/ejhigdbeeh . Robert Peter Gale MD, PhD & Mei-Jie Zhang PhD. The most important outcome of many studies of haematopoietic cell transplants is survival. The statistical field that deals with such outcomes is survival analysis. Methods developed in this field are also applicable to other outcomes where the occurrence and timing are important. Analysis of such time-to-event outcomes has special challenges because it takes time to observe time. The most important condition for unbiased estimation of a survival curve-non-informative censoring-is discussed along with methods to account for competing risks, a situation where multiple, mutually-exclusive endpoints are of interest. Techniques to compare survival outcomes between groups are reviewed, including the instance where it is unknown at baseline to which group a subject will belong later during follow-up (time-dependent covariates). Series Editor IntroductionThe final article in our Statistics Series by de Wreede and colleagues deals with the important issue of survival analyses in general and in recipients of haematopoietic cell transplants specifically. At first glance analyzing survival should be simple. The endpoint is clear with rare exception, the subject is either alive or dead. Compare this to other less well defined transplant-related outcomes such as who has acute graft-versus-host disease (GvHD) and of what grade or what is the cause of interstitial pneumonia. There is also the complexity of composite endpoints when one analyzes outcomes such as event-free (EFS) or relapse-free survival (RFS). Here you’re either alive or dead. Period. Alas, as it turns out things are not so simple. As the authours point out: it takes time to observe time. It is almost never possible to wait long enough for everyone in a study to die. (Some people who are cured by a transplant will outlive their physician and statistician.) Other subjects may not be followed until the end of the study, lost to follow-up or withdraw consent to participate. Often these are non-random events, muddy the water and make what seems a simple analysis of survival not so. Fortunately, de Wreede and colleagues discuss the issues of informative and non-informative censoring and time-dependent co-variates. And there are other nasty complexities such non-proportional hazards of death say when initially there is a survival disadvantage to transplants from transplant-related mortality followed in 1–2 years by a survival benefit. They emphasize the danger of considering only Hazard Ratio in this setting. Lastly, the authours discuss how to compare interventions such as conventional therapy versus a haematopoietic cell transplant when the endpoint of interest is survival. We think this article will be of considerable interest to readers of BONE MARROW TRANSPLANTATION and suggest you study it carefully. Survival analyses, seemingly simple, are a potential minefield. You don’t want to step on one. This article and the entire Statistics Series are available online at https://www.nature.com/collections/ejhigdbeeh.Robert Peter Gale MD, PhD & Mei-Jie Zhang PhD.The most important outcome of many studies of haematopoietic cell transplants is survival. The statistical field that deals with such outcomes is survival analysis. Methods developed in this field are also applicable to other outcomes where the occurrence and timing are important. Analysis of such time-to-event outcomes has special challenges because it takes time to observe time. The most important condition for unbiased estimation of a survival curve—non-informative censoring—is discussed along with methods to account for competing risks, a situation where multiple, mutually-exclusive endpoints are of interest. Techniques to compare survival outcomes between groups are reviewed, including the instance where it is unknown at baseline to which group a subject will belong later during follow-up (time-dependent covariates).Bell ringing coffin used in England in the Middle Ages in case of accidental burial. Popular with people with taphophobia. More information if you are interested at: https://www.vox.com/2015/7/31/9075011/buried-alive-safety-coffins. Series Editor Introduction The final article in our Statistics Series by de Wreede and colleagues deals with the important issue of survival analyses in general and in recipients of haematopoietic cell transplants specifically. At first glance analyzing survival should be simple. The endpoint is clear with rare exception, the subject is either alive or dead. Compare this to other less well defined transplant-related outcomes such as who has acute graft- versus -host disease (G v HD) and of what grade or what is the cause of interstitial pneumonia. There is also the complexity of composite endpoints when one analyzes outcomes such as event-free (EFS) or relapse-free survival (RFS). Here you’re either alive or dead. Period. Alas, as it turns out things are not so simple. As the authours point out: it takes time to observe time . It is almost never possible to wait long enough for everyone in a study to die. (Some people who are cured by a transplant will outlive their physician and statistician.) Other subjects may not be followed until the end of the study, lost to follow-up or withdraw consent to participate. Often these are non-random events, muddy the water and make what seems a simple analysis of survival not so. Fortunately, de Wreede and colleagues discuss the issues of informative and non-informative censoring and time-dependent co-variates. And there are other nasty complexities such non-proportional hazards of death say when initially there is a survival disadvantage to transplants from transplant-related mortality followed in 1–2 years by a survival benefit. They emphasize the danger of considering only Hazard Ratio in this setting. Lastly, the authours discuss how to compare interventions such as conventional therapy versus a haematopoietic cell transplant when the endpoint of interest is survival. We think this article will be of considerable interest to readers of BONE MARROW TRANSPLANTATION and suggest you study it carefully. Survival analyses, seemingly simple, are a potential minefield. You don’t want to step on one. This article and the entire Statistics Series are available online at https://www.nature.com/collections/ejhigdbeeh . Robert Peter Gale MD, PhD & Mei-Jie Zhang PhD. The most important outcome of many studies of haematopoietic cell transplants is survival. The statistical field that deals with such outcomes is survival analysis. Methods developed in this field are also applicable to other outcomes where the occurrence and timing are important. Analysis of such time-to-event outcomes has special challenges because it takes time to observe time . The most important condition for unbiased estimation of a survival curve—non-informative censoring—is discussed along with methods to account for competing risks, a situation where multiple, mutually-exclusive endpoints are of interest. Techniques to compare survival outcomes between groups are reviewed, including the instance where it is unknown at baseline to which group a subject will belong later during follow-up (time-dependent covariates). Bell ringing coffin used in England in the Middle Ages in case of accidental burial. Popular with people with taphophobia. More information if you are interested at: https://www.vox.com/2015/7/31/9075011/buried-alive-safety-coffins . |
| Author | Schetelig, Johannes Putter, Hein de Wreede, Liesbeth C. |
| Author_xml | – sequence: 1 givenname: Liesbeth C. orcidid: 0000-0002-7667-9369 surname: de Wreede fullname: de Wreede, Liesbeth C. email: l.c.de_wreede@lumc.nl organization: Department of Biomedical Data Sciences, LUMC, DKMS, German Bone Marrow Donor Center – sequence: 2 givenname: Johannes orcidid: 0000-0002-2780-2981 surname: Schetelig fullname: Schetelig, Johannes organization: DKMS, German Bone Marrow Donor Center, Medical Department I, University Hospital Carl Gustav Carus, Technische Universität Dresden – sequence: 3 givenname: Hein orcidid: 0000-0001-5395-1422 surname: Putter fullname: Putter, Hein organization: Department of Biomedical Data Sciences, LUMC |
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| Copyright | The Author(s), under exclusive licence to Springer Nature Limited 2022. corrected publication 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 2022. The Author(s), under exclusive licence to Springer Nature Limited. |
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The final article in our Statistics Series by de Wreede and colleagues deals with the important issue of survival analyses in... Series Editor IntroductionThe final article in our Statistics Series by de Wreede and colleagues deals with the important issue of survival analyses in general... The final article in our Statistics Series by de Wreede and colleagues deals with the important issue of survival analyses in general and in recipients of... |
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| SubjectTerms | 692/308/174 692/308/2171 692/499 692/699/1541/1990 692/700/1750/1976 Bone marrow Bone marrow transplantation Caskets Cell Biology Cell survival Graduate studies Graft-versus-host reaction Hazards Health hazards Hematology Internal Medicine Medicine Medicine & Public Health Middle Ages Public Health Statistical analysis Stem cell transplantation Stem Cells Survival Survival analysis Time dependence Transplantation Transplants Transplants & implants |
| Title | Analysis of survival outcomes in haematopoietic cell transplant studies: Pitfalls and solutions |
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