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Article ; Online: What is relative survival and what is its role in haematology?

Pohar Perme, Maja / de Wreede, Liesbeth C / Manevski, Damjan

Best practice & research. Clinical haematology

2023 Volume 36, Issue 2, Page(s) 101474

Abstract: In many haematological diseases, the survival probability is the key outcome. However, when the population of patients is rather old and the follow-up long, a significant proportion of deaths cannot be attributed to the studied disease. This lessens the ... ...

Abstract	In many haematological diseases, the survival probability is the key outcome. However, when the population of patients is rather old and the follow-up long, a significant proportion of deaths cannot be attributed to the studied disease. This lessens the importance of common survival analysis measures like overall survival and shows the need for other outcome measures requiring more complex methodology. When disease-specific information is of interest but the cause of death is not available in the data, relative survival methodology becomes crucial. The idea of relative survival is to merge the observed data set with the mortality data in the general population and thus allow for an indirect estimation of the burden of the disease. In this work, an overview of different measures that can be of interest in the field of haematology is given. We introduce the crude mortality that reports the probability of dying due to the disease of interest; the net survival that focuses on excess hazard alone and presents the key measure in comparing the disease burden of patients from populations with different general population mortality; and the relative survival ratio which gives a simple comparison of the patients' and the general population survival. We explain the properties of each measure, and some brief notes are given on estimation. Furthermore, we describe how association with covariates can be studied. All the methods and their estimators are illustrated on a sub-cohort of older patients who received a first allogeneic hematopoietic stem cell transplantation for myelodysplastic syndromes or secondary acute myeloid leukemia, to show how different methods can provide different insights into the data.
MeSH term(s)	Humans ; Leukemia, Myeloid, Acute/therapy ; Myelodysplastic Syndromes/therapy ; Survival Analysis ; Neoplasms, Second Primary ; Hematology ; Hematopoietic Stem Cell Transplantation/adverse effects ; Hematopoietic Stem Cell Transplantation/methods
Language	English
Publishing date	2023-05-08
Publishing country	Netherlands
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	2048027-1
ISSN	1532-1924 ; 1521-6926
ISSN (online)	1532-1924
ISSN	1521-6926
DOI	10.1016/j.beha.2023.101474
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Article ; Online: Handling missing covariate data in clinical studies in haematology.

Bonneville, Edouard F / Schetelig, Johannes / Putter, Hein / de Wreede, Liesbeth C

Best practice & research. Clinical haematology

2023 Volume 36, Issue 2, Page(s) 101477

Abstract: Missing data are frequently encountered across studies in clinical haematology. Failure to handle these missing values in an appropriate manner can complicate the interpretation of a study's findings, as estimates presented may be biased and/or imprecise. ...

Abstract	Missing data are frequently encountered across studies in clinical haematology. Failure to handle these missing values in an appropriate manner can complicate the interpretation of a study's findings, as estimates presented may be biased and/or imprecise. In the present work, we first provide an overview of current methods for handling missing covariate data, along with their advantages and disadvantages. Furthermore, a systematic review is presented, exploring both contemporary reporting of missing values in major haematological journals, and the methods used for handling them. A principal finding was that the method of handling missing data was explicitly specified in a minority of articles (in 76 out of 195 articles reporting missing values, 39%). Among these, complete case analysis and the missing indicator method were the most common approaches to dealing with missing values, with more complex methods such as multiple imputation being extremely rare (in 7 out of 195 articles). An example analysis (with associated code) is also provided using hematopoietic stem cell transplantation data, illustrating the different approaches to handling missing values. We conclude with various recommendations regarding the reporting and handling of missing values for future studies in clinical haematology.
MeSH term(s)	Humans ; Data Interpretation, Statistical ; Hematology ; Research Design
Language	English
Publishing date	2023-05-22
Publishing country	Netherlands
Document type	Systematic Review ; Journal Article ; Review
ZDB-ID	2048027-1
ISSN	1532-1924 ; 1521-6926
ISSN (online)	1532-1924
ISSN	1521-6926
DOI	10.1016/j.beha.2023.101477
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Article ; Online: Analysis of survival outcomes in haematopoietic cell transplant studies: Pitfalls and solutions.

de Wreede, Liesbeth C / Schetelig, Johannes / Putter, Hein

Bone marrow transplantation

2022 Volume 57, Issue 9, Page(s) 1428–1434

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 ... ...

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 (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).
MeSH term(s)	Bone Marrow Transplantation ; Graft vs Host Disease/etiology ; Hematopoietic Stem Cell Transplantation/adverse effects ; Humans ; Proportional Hazards Models ; Survival Analysis
Language	English
Publishing date	2022-06-27
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	632854-4
ISSN	1476-5365 ; 0268-3369 ; 0951-3078
ISSN (online)	1476-5365
ISSN	0268-3369 ; 0951-3078
DOI	10.1038/s41409-022-01740-4
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Article ; Online: Multiple imputation for cause-specific Cox models: Assessing methods for estimation and prediction.

Bonneville, Edouard F / Resche-Rigon, Matthieu / Schetelig, Johannes / Putter, Hein / de Wreede, Liesbeth C

Statistical methods in medical research

2022 Volume 31, Issue 10, Page(s) 1860–1880

Abstract: In studies analyzing competing time-to-event outcomes, interest often lies in both estimating the effects of baseline covariates on the cause-specific hazards and predicting cumulative incidence functions. When missing values occur in these baseline ... ...

Abstract	In studies analyzing competing time-to-event outcomes, interest often lies in both estimating the effects of baseline covariates on the cause-specific hazards and predicting cumulative incidence functions. When missing values occur in these baseline covariates, they may be discarded as part of a complete-case analysis or multiply imputed. In the latter case, the imputations may be performed either compatibly with a substantive model pre-specified as a cause-specific Cox model [substantive model compatible fully conditional specification (SMC-FCS)], or approximately so [multivariate imputation by chained equations (MICE)]. In a large simulation study, we assessed the performance of these three different methods in terms of estimating cause-specific regression coefficients and predicting cumulative incidence functions. Concerning regression coefficients, results provide further support for use of SMC-FCS over MICE, particularly when covariate effects are large and the baseline hazards of the competing events are substantially different. Complete-case analysis also shows adequate performance in settings where missingness is not outcome dependent. With regard to cumulative incidence prediction, SMC-FCS and MICE are performed more similarly, as also evidenced in the illustrative analysis of competing outcomes following a hematopoietic stem cell transplantation. The findings are discussed alongside recommendations for practising statisticians.
MeSH term(s)	Computer Simulation ; Data Interpretation, Statistical ; Models, Statistical ; Proportional Hazards Models
Language	English
Publishing date	2022-06-05
Publishing country	England
Document type	Journal Article
ZDB-ID	1136948-6
ISSN	1477-0334 ; 0962-2802
ISSN (online)	1477-0334
ISSN	0962-2802
DOI	10.1177/09622802221102623
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Article ; Online: Estimating distribution of length of stay in a multi-state model conditional on the pathway, with an application to patients hospitalised with Covid-19.

Keogh, Ruth H / Diaz-Ordaz, Karla / Jewell, Nicholas P / Semple, Malcolm G / de Wreede, Liesbeth C / Putter, Hein

Lifetime data analysis

2023 Volume 29, Issue 2, Page(s) 288–317

Abstract: Multi-state models are used to describe how individuals transition through different states over time. The distribution of the time spent in different states, referred to as 'length of stay', is often of interest. Methods for estimating expected length ... ...

Abstract	Multi-state models are used to describe how individuals transition through different states over time. The distribution of the time spent in different states, referred to as 'length of stay', is often of interest. Methods for estimating expected length of stay in a given state are well established. The focus of this paper is on the distribution of the time spent in different states conditional on the complete pathway taken through the states, which we call 'conditional length of stay'. This work is motivated by questions about length of stay in hospital wards and intensive care units among patients hospitalised due to Covid-19. Conditional length of stay estimates are useful as a way of summarising individuals' transitions through the multi-state model, and also as inputs to mathematical models used in planning hospital capacity requirements. We describe non-parametric methods for estimating conditional length of stay distributions in a multi-state model in the presence of censoring, including conditional expected length of stay (CELOS). Methods are described for an illness-death model and then for the more complex motivating example. The methods are assessed using a simulation study and shown to give unbiased estimates of CELOS, whereas naive estimates of CELOS based on empirical averages are biased in the presence of censoring. The methods are applied to estimate conditional length of stay distributions for individuals hospitalised due to Covid-19 in the UK, using data on 42,980 individuals hospitalised from March to July 2020 from the COVID19 Clinical Information Network.
MeSH term(s)	COVID-19 ; Length of Stay ; Humans ; Intensive Care Units ; Male ; Female ; Computer Simulation
Language	English
Publishing date	2023-02-08
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1479719-7
ISSN	1572-9249 ; 1380-7870
ISSN (online)	1572-9249
ISSN	1380-7870
DOI	10.1007/s10985-022-09586-0
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Article ; Online: Haplotype reconstruction for genetically complex regions with ambiguous genotype calls: Illustration by the KIR gene region.

van der Burg, Lars L J / de Wreede, Liesbeth C / Baldauf, Henning / Sauter, Jürgen / Schetelig, Johannes / Putter, Hein / Böhringer, Stefan

Genetic epidemiology

2023 Volume 48, Issue 1, Page(s) 3–26

Abstract: Advances in DNA sequencing technologies have enabled genotyping of complex genetic regions exhibiting copy number variation and high allelic diversity, yet it is impossible to derive exact genotypes in all cases, often resulting in ambiguous genotype ... ...

Abstract	Advances in DNA sequencing technologies have enabled genotyping of complex genetic regions exhibiting copy number variation and high allelic diversity, yet it is impossible to derive exact genotypes in all cases, often resulting in ambiguous genotype calls, that is, partially missing data. An example of such a gene region is the killer-cell immunoglobulin-like receptor (KIR) genes. These genes are of special interest in the context of allogeneic hematopoietic stem cell transplantation. For such complex gene regions, current haplotype reconstruction methods are not feasible as they cannot cope with the complexity of the data. We present an expectation-maximization (EM)-algorithm to estimate haplotype frequencies (HTFs) which deals with the missing data components, and takes into account linkage disequilibrium (LD) between genes. To cope with the exponential increase in the number of haplotypes as genes are added, we add three components to a standard EM-algorithm implementation. First, reconstruction is performed iteratively, adding one gene at a time. Second, after each step, haplotypes with frequencies below a threshold are collapsed in a rare haplotype group. Third, the HTF of the rare haplotype group is profiled in subsequent iterations to improve estimates. A simulation study evaluates the effect of combining information of multiple genes on the estimates of these frequencies. We show that estimated HTFs are approximately unbiased. Our simulation study shows that the EM-algorithm is able to combine information from multiple genes when LD is high, whereas increased ambiguity levels increase bias. Linear regression models based on this EM, show that a large number of haplotypes can be problematic for unbiased effect size estimation and that models need to be sparse. In a real data analysis of KIR genotypes, we compare HTFs to those obtained in an independent study. Our new EM-algorithm-based method is the first to account for the full genetic architecture of complex gene regions, such as the KIR gene region. This algorithm can handle the numerous observed ambiguities, and allows for the collapsing of haplotypes to perform implicit dimension reduction. Combining information from multiple genes improves haplotype reconstruction.
MeSH term(s)	Humans ; Haplotypes ; DNA Copy Number Variations ; Gene Frequency ; Models, Genetic ; Genotype
Language	English
Publishing date	2023-10-13
Publishing country	United States
Document type	Journal Article
ZDB-ID	605785-8
ISSN	1098-2272 ; 0741-0395
ISSN (online)	1098-2272
ISSN	0741-0395
DOI	10.1002/gepi.22538
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Article ; Online: Construction and assessment of prediction rules for binary outcome in the presence of missing predictor data using multiple imputation and cross-validation: Methodological approach and data-based evaluation.

Mertens, Bart J A / Banzato, Erika / de Wreede, Liesbeth C

Biometrical journal. Biometrische Zeitschrift

2020 Volume 62, Issue 3, Page(s) 724–741

Abstract: We investigate calibration and assessment of predictive rules when missing values are present in the predictors. Our paper has two key objectives. The first is to investigate how the calibration of the prediction rule can be combined with use of multiple ...

Abstract	We investigate calibration and assessment of predictive rules when missing values are present in the predictors. Our paper has two key objectives. The first is to investigate how the calibration of the prediction rule can be combined with use of multiple imputation to account for missing predictor observations. The second objective is to propose such methods that can be implemented with current multiple imputation software, while allowing for unbiased predictive assessment through validation on new observations for which outcome is not yet available. We commence with a review of the methodological foundations of multiple imputation as a model estimation approach as opposed to a purely algorithmic description. We specifically contrast application of multiple imputation for parameter (effect) estimation with predictive calibration. Based on this review, two approaches are formulated, of which the second utilizes application of the classical Rubin's rules for parameter estimation, while the first approach averages probabilities from models fitted on single imputations to directly approximate the predictive density for future observations. We present implementations using current software that allow for validation and estimation of performance measures by cross-validation, as well as imputation of missing data in predictors on the future data where outcome is missing by definition. To simplify, we restrict discussion to binary outcome and logistic regression throughout. Method performance is verified through application on two real data sets. Accuracy (Brier score) and variance of predicted probabilities are investigated. Results show substantial reductions in variation of calibrated probabilities when using the first approach.
MeSH term(s)	Analysis of Variance ; Biometry/methods ; Calibration
Language	English
Publishing date	2020-02-13
Publishing country	Germany
Document type	Journal Article
ZDB-ID	131640-0
ISSN	1521-4036 ; 0323-3847 ; 0006-3452
ISSN (online)	1521-4036
ISSN	0323-3847 ; 0006-3452
DOI	10.1002/bimj.201800289
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Article ; Online: Personalized Decision Making on Genomic Testing in Early Breast Cancer: Expanding the MINDACT Trial with Decision-Analytic Modeling.

Steyerberg, Ewout W / de Wreede, Liesbeth C / van Klaveren, David / Bossuyt, Patrick M M

Medical decision making : an international journal of the Society for Medical Decision Making

2021 Volume 41, Issue 3, Page(s) 354–365

Abstract: Background: Genomic tests may improve upon clinical risk estimation with traditional prognostic factors. We aimed to explore how evidence on the prognostic strength of a genomic signature (clinical validity) can contribute to individualized decision ... ...

Abstract	Background: Genomic tests may improve upon clinical risk estimation with traditional prognostic factors. We aimed to explore how evidence on the prognostic strength of a genomic signature (clinical validity) can contribute to individualized decision making on starting chemotherapy for women with breast cancer (clinical utility). Methods: The MINDACT trial was a randomized trial that enrolled 6693 women with early-stage breast cancer. A 70-gene signature (Mammaprint) was used to estimate genomic risk, and clinical risk was estimated by a dichotomized version of the Adjuvant!Online risk calculator. Women with discordant risk results were randomized to the use of chemotherapy. We simulated the full risk distribution of these women and estimated individual benefit, assuming a constant relative effect of chemotherapy. Results: The trial showed a prognostic effect of the genomic signature (adjusted hazard ratio 2.4). A decision-analytic modeling approach identified far fewer women as candidates for genetic testing (4% rather than 50%) and fewer benefiting from chemotherapy (3% rather than 27%) as compared with the MINDACT trial report. The selection of women benefitting from genetic testing and chemotherapy depended strongly on the required benefit from treatment and the assumed therapeutic effect of chemotherapy. Conclusions: A high-quality pragmatic trial was insufficient to directly inform clinical practice on the utility of a genomic test for individual women. The indication for genomic testing may be far more limited than suggested by the MINDACT trial.
MeSH term(s)	Breast Neoplasms/drug therapy ; Breast Neoplasms/genetics ; Chemotherapy, Adjuvant ; Decision Making ; Female ; Genetic Testing ; Humans ; Prognosis
Language	English
Publishing date	2021-03-03
Publishing country	United States
Document type	Journal Article ; Randomized Controlled Trial
ZDB-ID	604497-9
ISSN	1552-681X ; 0272-989X
ISSN (online)	1552-681X
ISSN	0272-989X
DOI	10.1177/0272989X21991173
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Article ; Online: Integrating relative survival in multi-state models-a non-parametric approach.

Manevski, Damjan / Putter, Hein / Pohar Perme, Maja / Bonneville, Edouard F / Schetelig, Johannes / de Wreede, Liesbeth C

Statistical methods in medical research

2022 Volume 31, Issue 6, Page(s) 997–1012

Abstract: Multi-state models provide an extension of the usual survival/event-history analysis setting. In the medical domain, multi-state models give the possibility of further investigating intermediate events such as relapse and remission. In this work, a ... ...

Abstract	Multi-state models provide an extension of the usual survival/event-history analysis setting. In the medical domain, multi-state models give the possibility of further investigating intermediate events such as relapse and remission. In this work, a further extension is proposed using relative survival, where mortality due to population causes (i.e. non-disease-related mortality) is evaluated. The objective is to split all mortality in disease and non-disease-related mortality, with and without intermediate events, in datasets where cause of death is not recorded or is uncertain. To this end, population mortality tables are integrated into the estimation process, while using the basic relative survival idea that the overall mortality hazard can be written as a sum of a population and an excess part. Hence, we propose an upgraded non-parametric approach to estimation, where population mortality is taken into account. Precise definitions and suitable estimators are given for both the transition hazards and probabilities. Variance estimating techniques and confidence intervals are introduced and the behaviour of the new method is investigated through simulations. The newly developed methodology is illustrated by the analysis of a cohort of patients followed after an allogeneic hematopoietic stem cell transplantation. The work has been implemented in the R package mstate.
MeSH term(s)	Hematopoietic Stem Cell Transplantation ; Humans ; Probability ; Proportional Hazards Models ; Recurrence ; Research Design ; Survival Analysis
Language	English
Publishing date	2022-03-14
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1136948-6
ISSN	1477-0334 ; 0962-2802
ISSN (online)	1477-0334
ISSN	0962-2802
DOI	10.1177/09622802221074156
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Article ; Online: Risk factors for graft-versus-host-disease after donor lymphocyte infusion following T-cell depleted allogeneic stem cell transplantation.

Koster, Eva A S / von dem Borne, Peter A / van Balen, Peter / Marijt, Erik W A / Tjon, Jennifer M L / Snijders, Tjeerd J F / van Lammeren, Daniëlle / Veelken, Hendrik / Falkenburg, J H Frederik / Halkes, Constantijn J M / de Wreede, Liesbeth C

Frontiers in immunology

2024 Volume 15, Page(s) 1335341

Abstract: Introduction: Unmodified donor lymphocyte infusions (DLI) after allogeneic stem cell transplantation (alloSCT) can boost the beneficial Graft-versus-Leukemia (GvL) effect but may also induce severe Graft-versus-Host-Disease (GvHD). To improve the ... ...

Abstract	Introduction: Unmodified donor lymphocyte infusions (DLI) after allogeneic stem cell transplantation (alloSCT) can boost the beneficial Graft-versus-Leukemia (GvL) effect but may also induce severe Graft-versus-Host-Disease (GvHD). To improve the balance between GvL and GvHD, it is crucial to identify factors that influence the alloreactivity of DLI. Methods: We investigated the effects of the presence of patient-derived antigen-presenting cells at time of DLI as estimated by the bone marrow (BM) chimerism status, lymphopenia as measured by the absolute lymphocyte count (ALC) at time of DLI, and the presence of a viral infection ( Results: For both DLIs, patients with reduced-intensity conditioning and an unrelated donor had the highest risk of GvHD. For DLI given at three months, viral infection within 1 week before and 2 weeks after DLI was an additional significant risk factor (hazard ratio (HR) 3.66 compared to no viral infection) for GvHD. At six months after alloSCT, viral infections were rare and not associated with GvHD. In contrast, mixed BM chimerism (HR 3.63 for ≥5% mixed chimerism compared to full donor) was an important risk factor for GvHD after DLI given at six months after alloSCT. ALC of <1000x10 Conclusion: These data demonstrate that the risk factors for GvHD after DLI depend on the setting of the DLI.
MeSH term(s)	Humans ; T-Lymphocytes ; Lymphocyte Transfusion/adverse effects ; Hematopoietic Stem Cell Transplantation/adverse effects ; Graft vs Host Disease/etiology ; Graft vs Host Disease/prevention & control ; Leukemia, Myeloid, Acute/complications ; Unrelated Donors ; Virus Diseases/complications
Language	English
Publishing date	2024-03-13
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2606827-8
ISSN	1664-3224 ; 1664-3224
ISSN (online)	1664-3224
ISSN	1664-3224
DOI	10.3389/fimmu.2024.1335341
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