Liver transplantation provides survival benefit at all levels of frailty: From the Multicenter Functional Assessment in Liver Transplantation Study.

Background And Aims: Offering LT to frail patients may reduce waitlist mortality but may increase post-LT mortality. LT survival benefit is the concept of balancing these risks. We sought to quantify the net survival benefit with LT by liver frailty index (LFI).

Associations between hospital-level socioeconomic patient mix and rates of central line-associated bloodstream infections in short bowel syndrome: A retrospective cohort study.

Background: Low neighborhood income is linked with increased hospitalizations for central line-associated bloodstream infections (CLABSIs) in pediatric short bowel syndrome (SBS). We assessed whether this relationship varies by hospital center.

Methods: We performed a retrospective cohort study using the Pediatric Health Information System (2018-2023) database for patients <18 years old with SBS (N = 1210) at 24 hospitals in the United States.

Misstatements, misperceptions, and mistakes in controlling for covariates in observational research.

We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection.

The influence of neighborhood income on healthcare utilization in pediatric liver transplant.

Objectives: Neighborhood contextual factors are associated with liver transplant outcomes. We analyzed associations between neighborhood-level socioeconomic status and healthcare utilization for pediatric liver transplant recipients.

Methods: We merged the Pediatric Health Information System and Scientific Registry of Transplant Recipients databases and included liver transplant recipients ≤21 years hospitalized between January 2004 and May 2022.

Outpatient mean arterial pressure: A potentially modifiable risk for acute kidney injury and death among patients with cirrhosis.

Mean arterial blood pressure (MAP), which decreases as portal hypertension progresses, may be a modifiable risk factor among patients with cirrhosis. We included adults enrolled in the Functional Assessment in Liver Transplantation study. We completed latent class trajectory analyses to define MAP trajectories.

A modified Hospital Frailty Risk Score for patients with cirrhosis undergoing abdominal operations.

Background And Aims: Existing tools for perioperative risk stratification in patients with cirrhosis do not incorporate measures of comorbidity. The Hospital Frailty Risk Score (HFRS) is a widely used measure of comorbidity burden in administrative dataset analyses. However, it is not specific to patients with cirrhosis, and application of this index is limited by its complexity.

Patient Profiling: Determining the Effects of Patient Factors on Vocal Fatigue.

Objectives: The phenomenon of vocal fatigue and the types of patients that are at greatest risk for vocal fatigue are not fully understood. The goal was to investigate patient profiles such as voice disorder type, demographics (age and gender), singing identity, interoceptive awareness, and psychosocial impacts on the severity of vocal fatigue.

Study Design: Prospective cohort study.

DYNAMIC RISK PREDICTION TRIGGERED BY INTERMEDIATE EVENTS USING SURVIVAL TREE ENSEMBLES.

With the availability of massive amounts of data from electronic health records and registry databases, incorporating time-varying patient information to improve risk prediction has attracted great attention. To exploit the growing amount of predictor information over time, we develop a unified framework for landmark prediction using survival tree ensembles, where an updated prediction can be performed when new information becomes available. Compared to conventional landmark prediction with fixed landmark times, our methods allow the landmark times to be subject-specific and triggered by an intermediate clinical event.

Exploring the complexity and spectrum of racial/ethnic disparities in colon cancer management.

Background: Colorectal cancer is a leading cause of morbidity and mortality across U.S. racial/ethnic groups.

Interoception, Voice Symptom Reporting, and Voice Disorders.

Objectives: Interoception may play a role in how individuals perceive their voice disorder. The first objective of this study was to investigate relationships between interoception and voice disorder class (functional, structural, neurological). The second objective was to determine relationships between interoception and voice-related outcome measures between patients with functional voice and upper airway disorders and typical voice users.

Association of Distance, Region, and Insurance With Advanced Colon Cancer at Initial Diagnosis.

This cross-sectional study examines factors associated with the stage at which colon cancer is diagnosed.

SAFARI: shape analysis for AI-segmented images.

Background: Recent developments to segment and characterize the regions of interest (ROI) within medical images have led to promising shape analysis studies. However, the procedures to analyze the ROI are arbitrary and vary by study. A tool to translate the ROI to analyzable shape representations and features is greatly needed.

Analysis of survival data with cure fraction and variable selection: A pseudo-observations approach.

In biomedical studies, survival data with a cure fraction (the proportion of subjects cured of disease) are commonly encountered. The mixture cure and bounded cumulative hazard models are two main types of cure fraction models when analyzing survival data with long-term survivors. In this article, in the framework of the Cox proportional hazards mixture cure model and bounded cumulative hazard model, we propose several estimators utilizing pseudo-observations to assess the effects of covariates on the cure rate and the risk of having the event of interest for survival data with a cure fraction.

Transformation model based regression with dependently truncated and independently censored data.

Truncated survival data arise when the event time is observed only if it falls within a subject specific region. The conventional risk-set adjusted Kaplan-Meier estimator or Cox model can be used for estimation of the event time distribution or regression coefficient. However, the validity of these approaches relies on the assumption of quasi-independence between truncation and event times.

Statistical inference on shape and size indexes for counting processes.

Single-index models have gained increased popularity in time-to-event analysis owing to their model flexibility and advantage in dimension reduction. We propose a semiparametric framework for the rate function of a recurrent event counting process by modelling its size and shape components with single-index models. With additional monotone constraints on the two link functions for the size and shape components, the proposed model possesses the desired directional interpretability of covariate effects and encompasses many commonly used models as special cases.

Nonparametric estimation of the survival distribution under covariate-induced dependent truncation.

There is often delayed entry into observational studies, which results in left truncation. In the estimation of the distribution of time-to-event from left-truncated data, standard survival analysis methods require quasi-independence between the truncation time and event time. Incorrectly assuming quasi-independence may lead to biased estimation.

Generalized scale-change models for recurrent event processes under informative censoring.

Two major challenges arise in regression analyses of recurrent event data: first, popular existing models, such as the Cox proportional rates model, may not fully capture the covariate effects on the underlying recurrent event process; second, the censoring time remains informative about the risk of experiencing recurrent events after accounting for covariates. We tackle both challenges by a general class of semiparametric scale-change models that allow a scale-change covariate effect as well as a multiplicative covariate effect. The proposed model is flexible and includes several existing models as special cases, such as the popular proportional rates model, the accelerated mean model, and the accelerated rate model.

ROC-guided survival trees and ensembles.

Tree-based methods are popular nonparametric tools in studying time-to-event outcomes. In this article, we introduce a novel framework for survival trees and ensembles, where the trees partition the dynamic survivor population and can handle time-dependent covariates. Using the idea of randomized tests, we develop generalized time-dependent receiver operating characteristic (ROC) curves for evaluating the performance of survival trees.

The missing indicator approach for censored covariates subject to limit of detection in logistic regression models.

Purpose: In several biomedical studies, one or more exposures of interest may be subject to nonrandom missingness because of the failure of the measurement assay at levels below its limit of detection. This issue is commonly encountered in studies of the metabolome using tandem mass spectrometry-based technologies. Owing to a large number of metabolites measured in these studies, preserving statistical power is of utmost interest.

Semiparametric Regression Analysis of Panel Count Data: A Practical Review.

Panel count data arise in many applications when the event history of a recurrent event process is only examined at a sequence of discrete time points. In spite of the recent methodological developments, the availability of their software implementations has been rather limited. Focusing on a practical setting where the effects of some time-independent covariates on the recurrent events are of primary interest, we review semiparametric regression modelling approaches for panel count data that have been implemented in R package spef.

Permutation Tests for General Dependent Truncation.

Truncated survival data arise when the event time is observed only if it falls within a subject-specific region, known as the truncation set. Left-truncated data arise when there is delayed entry into a study, such that subjects are included only if their event time exceeds some other time. of truncation and failure refers to factorization of their joint density in the observable region.

Transformation model estimation of survival under dependent truncation and independent censoring.

Truncation is a mechanism that permits observation of selected subjects from a source population; subjects are excluded if their event times are not contained within subject-specific intervals. Standard survival analysis methods for estimation of the distribution of the event time require of failure and truncation. When quasi-independence does not hold, alternative estimation procedures are required; currently, there is a copula model approach that makes strong modeling assumptions, and a transformation model approach that does not allow for right censoring.

Wide Range of Clinical Outcomes in Patients with Gliomatosis Cerebri Growth Pattern: A Clinical, Radiographic, and Histopathologic Study.

Background: The 2016 World Health Organization Classification of Central Nervous System Tumors categorizes gliomatosis cerebri growth pattern (GC) as a subgroup of diffuse infiltrating gliomas, defined by extent of brain involvement on magnetic resonance imaging (MRI). Clinical and radiographic features in GC patients are highly heterogeneous; however, prognosis has historically been considered poor.

Subjects, Materials, And Methods: We performed a retrospective search for patients at our institution meeting radiographic criteria of primary, type I GC (defined as diffuse tumor infiltration without associated tumor mass and contrast enhancement on MRI) and analyzed their clinical, imaging, and histopathologic features.

Threshold regression to accommodate a censored covariate.

In several common study designs, regression modeling is complicated by the presence of censored covariates. Examples of such covariates include maternal age of onset of dementia that may be right censored in an Alzheimer's amyloid imaging study of healthy subjects, metabolite measurements that are subject to limit of detection censoring in a case-control study of cardiovascular disease, and progressive biomarkers whose baseline values are of interest, but are measured post-baseline in longitudinal neuropsychological studies of Alzheimer's disease. We propose threshold regression approaches for linear regression models with a covariate that is subject to random censoring.