Competing risks regression for clustered data with covariate-dependent censoring.

Competing risks data in clinical trial or observational studies often suffer from cluster effects such as center effects and matched pairs design. The proportional subdistribution hazards (PSH) model is one of the most widely used methods for competing risks data analyses. However, the current literature on the PSH model for clustered competing risks data is limited to covariate-independent censoring and the unstratified model.

Adjusted curves for clustered survival and competing risks data.

Observational studies with right-censored data often have clustered data due to matched pairs or a study center effect. In such data, there may be an imbalance in patient characteristics between treatment groups, where Kaplan-Meier curves or unadjusted cumulative incidence curves can be misleading and may not represent the average patient on a given treatment arm. Adjusted curves are desirable to appropriately display survival or cumulative incidence curves in this case.

Joint Screening for Ultra-High Dimensional Multi-Omics Data.

Investigators often face ultra-high dimensional multi-omics data, where identifying significant genes and omics within a gene is of interest. In such data, each gene forms a group consisting of its multiple omics. Moreover, some genes may also be highly correlated.

Test Statistics and Statistical Inference for Data With Informative Cluster Sizes.

In biomedical studies, investigators often encounter clustered data. The cluster sizes are said to be informative if the outcome depends on the cluster size. Ignoring informative cluster sizes in the analysis leads to biased parameter estimation in marginal and mixed-effect regression models.

Meta-Analytic Gene-Clustering Algorithm for Integrating Multi-Omics and Multi-Study Data.

Gene pathways and gene-regulatory networks are used to describe the causal relationship between genes, based on biological experiments. However, many genes are still to be studied to define novel pathways. To address this, a gene-clustering algorithm has been used to group correlated genes together, based on the similarity of their gene expression level.

Efficient estimation for left-truncated competing risks regression for case-cohort studies.

The case-cohort study design provides a cost-effective study design for a large cohort study with competing risk outcomes. The proportional subdistribution hazards model is widely used to estimate direct covariate effects on the cumulative incidence function for competing risk data. In biomedical studies, left truncation often occurs and brings extra challenges to the analysis.

Asthma as a Risk Factor for Hospitalization in Children and Youth With COVID-19: A Retrospective Cohort Study.

Background: Studies examining the association between asthma and hospitalization among children and youth with coronavirus disease 2019 (COVID-19) have yielded mixed results. Both asthma and COVID-19 hospitalization are characterized by racial, ethnic and socioeconomic disparities which also pattern geographically, yet no studies to date have adjusted for neighborhood context in the assessment of this association.

Methods: Mixed effects logistic regression was used to estimate the association between asthma and hospitalization due to COVID-19 in a sample of 28,997 children and youth diagnosed with COVID-19 in Milwaukee County, Wisconsin, from March 1, 2020, to May 31, 2022.

The analysis of multiple outcomes, multiple variables and variables selection in hematopoietic cell transplantation studies.

It is common to study time-to-event data in cancer research such as hematopoietic cell transplantation (HCT) for leukemia. The extensive work has been done for the univariate survival outcome, that is, one event type. However, in practice a subject is often exposed to multiple types of outcomes.

Sample size and power determination for multiparameter evaluation in nonlinear regression models with potential stratification.

Sample size and power determination are crucial design considerations for biomedical studies intending to formally test the effects of key variables on an outcome. Other known prognostic factors may exist, necessitating the use of techniques for covariate adjustment when conducting this evaluation. Moreover, the main interest often includes assessing the impact of more than one variable on an outcome, such as multiple treatments or risk factors.

Clinical impact of cryopreservation of allogeneic hematopoietic cell grafts during the onset of the COVID-19 pandemic.

At the onset of the COVID-19 pandemic, the National Marrow Donor Program mandated the cryopreservation of hematopoietic cell grafts from volunteer unrelated donors because of numerous patient and donor safety concerns and logistical hurdles. Using the Center for International Blood and Marrow Transplant Research outcomes database, we report the impact of cryopreservation on overall survival (OS) and other outcomes within 1 year after hematopoietic cell transplantation (HCT). We analyzed 1543 recipients of cryopreserved allografts receiving HCT at US centers during the first 6 months of the pandemic and compared them with 2499 recipients of fresh allografts during a 6-month period in 2019.

Effect of Autograft CD34 Dose on Outcome in Pediatric Patients Undergoing Autologous Hematopoietic Stem Cell Transplant for Central Nervous System Tumors.

Consolidation with autologous hematopoietic stem cell transplantation (HSCT) has improved survival for patients with central nervous system tumors (CNSTs). The impact of the autologous graft CD34+ dose on patient outcomes is unknown. We wanted to analyze the relationship between CD34 dose, total nucleated cell (TNC) dose, and clinical outcomes, including overall survival (OS), progression-free survival (PFS), relapse, non-relapse mortality (NRM), endothelial-injury complications (EIC), and time to neutrophil engraftment in children undergoing autologous HSCT for CNSTs.

Marginal semiparametric transformation models for clustered multivariate competing risks data.

Multivariate survival models are often used in studying multiple outcomes for right-censored data. However, the outcomes of interest often have competing risks, where standard multivariate survival models may lead to invalid inferences. For example, patients who had stem cell transplantation may experience multiple types of infections after transplant while reconstituting their immune system, where death without experiencing infections is a competing risk for infections.

The Impact of Pre-Apheresis Health Related Quality of Life on Peripheral Blood Progenitor Cell Yield and Donor's Health and Outcome: Secondary Analysis of Patient-Reported Outcome Data from the RDSafe and BMT CTN 0201 Clinical Trials.

There is a lack of evidence about how health-related quality of life (HRQoL), including psychosocial factors, might affect donation-related experiences and clinical markers in the context of hematopoietic stem cell donation. The broader literature suggests that psychological factors, including anxiety and depression, are associated with higher levels of inflammatory burden leading to poorer postprocedural outcomes including longer hospital stays and increased pain perception. In this study, we aimed to evaluate whether predonation HRQoL markers predict toxicity profile and stem cell yield after peripheral blood stem cell (PBSC) donation in healthy donors.

Does recipient body mass index inform donor selection for allogeneic haematopoietic cell transplantation?

It is not known whether obesity has a differential effect on allogeneic haematopoietic cell transplantation outcomes with alternative donor types. We report the results of a retrospective registry study examining the effect of obesity [body mass index (BMI) > 30] on outcomes with alternative donors (haploidentical related donor with two or more mismatches and receiving post-transplant cyclophosphamide [haplo] and cord blood (CBU)] versus matched unrelated donor (MUD). Adult patients receiving haematopoietic cell transplantation for haematologic malignancy (2013-2017) (N = 16 182) using MUD (n = 11 801), haplo (n = 2894) and CBU (n = 1487) were included.

Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design.

A generalized case-cohort design has been used when measuring exposures is expensive and events are not rare in the full cohort. This design collects expensive exposure information from a (stratified) randomly selected subset from the full cohort, called the subcohort, and a fraction of cases outside the subcohort. For the full cohort study with competing risks, He et al.

A calibrated Bayesian method for the stratified proportional hazards model with missing covariates.

Missing covariates are commonly encountered when evaluating covariate effects on survival outcomes. Excluding missing data from the analysis may lead to biased parameter estimation and a misleading conclusion. The inverse probability weighting method is widely used to handle missing covariates.

Autologous transplant vs chimeric antigen receptor T-cell therapy for relapsed DLBCL in partial remission.

The relative efficacy of autologous hematopoietic cell transplant (auto-HCT) vs chimeric antigen receptor T-cell (CAR-T) therapy in patients with diffuse large B-cell lymphoma (DLBCL) who achieve a partial remission (PR) after salvage chemotherapy is not known. Using the Center for International Blood & Marrow Transplant Research registry database, we identified adult patients with DLBCL who received either an auto-HCT (2013-2019) or CAR-T treatment with axicabtagene ciloleucel (2018-2019) while in a PR by computed tomography or positron emission tomography scan. We compared the clinical outcomes between the 2 cohorts using univariable and multivariable regression models after adjustment for relevant baseline and clinical factors.