Health Utility Survival for Randomized Clinical Trials: Extensions and Statistical Properties.

Overall survival has been used as the primary endpoint for many randomized trials that aim to examine whether a new treatment is non-inferior to the standard treatment or placebo control. When a new treatment is indeed non-inferior in terms of survival, it may be important to assess other outcomes including health utility. However, analyzing health utility scores in a secondary analysis may have limited power since the primary objectives of the original study design may not include health utility.

Personalized ctDNA monitoring in metastatic HR+/HER2- breast cancer patients during endocrine and CDK4/6 inhibitor therapy.

Improved methods to monitor treatment response may enhance patient management and clinical outcomes. This study assessed the feasibility and performance of a tumor-informed circulating tumor DNA (ctDNA) assay in metastatic HR+/HER2- breast cancer patients receiving endocrine and CDK4/6 inhibitor therapy. By conducting whole exome sequencing on archival tumors, highly sensitive personalized ctDNA panels were designed for blood monitoring.

Health utility adjusted survival: A composite endpoint for clinical trial designs.

Many randomized trials have used overall survival as the primary endpoint for establishing non-inferiority of one treatment compared to another. However, if a treatment is non-inferior to another treatment in terms of overall survival, clinicians may be interested in further exploring which treatment results in better health utility scores for patients. Examining health utility in a secondary analysis is feasible, however, since health utility is not the primary endpoint, it is usually not considered in the sample size calculation, hence the power to detect a difference of health utility is not guaranteed.

Real-world treatment outcomes and clinicopathologic and molecular determinants of response to first-line lenvatinib in patients with advanced follicular cell-derived thyroid cancer.

Background: There is a paucity of real-world data regarding lenvatinib for locally-recurrent, metastatic and RAI-refractory thyroid cancer. Here we examined the efficacy of first-line lenvatinib in a genomically-characterized cohort and identified clinicopathological/molecular correlates of drug response.

Methods: Patients with advanced follicular cell-derived thyroid cancer who underwent NGS at Princess Margaret Cancer Centre and commenced first-line lenvatinib monotherapy between 2015-2023 were included.

Accuracy of Large Language Model-based Automatic Calculation of Ovarian-Adnexal Reporting and Data System MRI Scores from Pelvic MRI Reports.

Background Ovarian-Adnexal Reporting and Data System (O-RADS) for MRI helps assign malignancy risk, but radiologist adoption is inconsistent. Automatic assignment of O-RADS scores from reports could increase adoption and accuracy. Purpose To evaluate the accuracy of large language models (LLMs), after strategic optimization, for automatically calculating O-RADS scores from reports.

Leveraging Large Language Models to Generate Clinical Histories for Oncologic Imaging Requisitions.

Background Clinical information improves imaging interpretation, but physician-provided histories on requisitions for oncologic imaging often lack key details. Purpose To evaluate large language models (LLMs) for automatically generating clinical histories for oncologic imaging requisitions from clinical notes and compare them with original requisition histories. Materials and Methods In total, 207 patients with CT performed at a cancer center from January to November 2023 and with an electronic health record clinical note coinciding with ordering date were randomly selected.

Large Language Models for Automated Synoptic Reports and Resectability Categorization in Pancreatic Cancer.

Background Structured radiology reports for pancreatic ductal adenocarcinoma (PDAC) improve surgical decision-making over free-text reports, but radiologist adoption is variable. Resectability criteria are applied inconsistently. Purpose To evaluate the performance of large language models (LLMs) in automatically creating PDAC synoptic reports from original reports and to explore performance in categorizing tumor resectability.

Length of Stay Prediction Models for Oral Cancer Surgery: Machine Learning, Statistical and ACS-NSQIP.

Objective: Accurate prediction of hospital length of stay (LOS) following surgical management of oral cavity cancer (OCC) may be associated with improved patient counseling, hospital resource utilization and cost. The objective of this study was to compare the performance of statistical models, a machine learning (ML) model, and The American College of Surgeons National Surgical Quality Improvement Program's (ACS-NSQIP) calculator in predicting LOS following surgery for OCC.

Materials And Methods: A retrospective multicenter database study was performed at two major academic head and neck cancer centers.

Two-stage multivariate Mendelian randomization on multiple outcomes with mixed distributions.

In clinical research, it is important to study whether certain clinical factors or exposures have causal effects on clinical and patient-reported outcomes such as toxicities, quality of life, and self-reported symptoms, which can help improve patient care. Usually, such outcomes are recorded as multiple variables with different distributions. Mendelian randomization (MR) is a commonly used technique for causal inference with the help of genetic instrumental variables to deal with observed and unobserved confounders.

A Bayesian approach for two-stage multivariate Mendelian randomization with mixed outcomes.

Many research studies have investigated the relationship between baseline factors or exposures, such as patient demographic and disease characteristics, and study outcomes such as toxicities or quality of life, but results from most of these studies may be problematic because of potential confounding effects (eg, the imbalance in baseline factors or exposures). It is important to study whether the baseline factors or exposures have causal effects on the clinical outcomes, so that clinicians can have better understanding of the diseases and develop personalized medicine. Mendelian randomization (MR) provides an efficient way to estimate the causal effects using genetic instrumental variables to handle confounders, but most of the existing studies focus on a single outcome at a time and ignores the correlation structure of multiple outcomes.

Oesophagectomy following noncurative endoscopic resection for oesophageal carcinoma: does interval matter?

Objectives: Oesophagectomy was always recommended after noncurative endoscopic resection (ER). And the optimal time interval from ER to oesophagectomy remains unclear. This study was to explore the effect of interval on pathologic stage and prognosis.

Clinical significance of clonal hematopoiesis in the setting of autologous stem cell transplantation for lymphoma.

Autologous stem cell transplantation (ASCT) remains a key therapeutic strategy for treating patients with relapsed or refractory non-Hodgkin and Hodgkin lymphoma. Clonal hematopoiesis (CH) has been proposed as a major contributor not only to the development of therapy-related myeloid neoplasms but also to inferior overall survival (OS) in patients who had undergone ASCT. Herein, we aimed to investigate the prognostic implications of CH after ASCT in a cohort of 420 lymphoma patients using ultra-deep, highly sensitive error-correction sequencing.

Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model.

With the increasing availability of large-scale GWAS summary data on various traits, Mendelian randomization (MR) has become commonly used to infer causality between a pair of traits, an exposure and an outcome. It depends on using genetic variants, typically SNPs, as instrumental variables (IVs). The inverse-variance weighted (IVW) method (with a fixed-effect meta-analysis model) is most powerful when all IVs are valid; however, when horizontal pleiotropy is present, it may lead to biased inference.

Model checking via testing for direct effects in Mendelian Randomization and transcriptome-wide association studies.

It is of great interest and potential to discover causal relationships between pairs of exposures and outcomes using genetic variants as instrumental variables (IVs) to deal with hidden confounding in observational studies. Two most popular approaches are Mendelian randomization (MR), which usually use independent genetic variants/SNPs across the genome, and transcriptome-wide association studies (TWAS) (or their generalizations) using cis-SNPs local to a gene (or some genome-wide and likely dependent SNPs), as IVs. In spite of their many promising applications, both approaches face a major challenge: the validity of their causal conclusions depends on three critical assumptions on valid IVs, and more generally on other modeling assumptions, which however may not hold in practice.

Speeding up Monte Carlo simulations for the adaptive sum of powered score test with importance sampling.

A central but challenging problem in genetic studies is to test for (usually weak) associations between a complex trait (e.g., a disease status) and sets of multiple genetic variants.

Implicating causal brain imaging endophenotypes in Alzheimer's disease using multivariable IWAS and GWAS summary data.

Recent evidence suggests the existence of many undiscovered heritable brain phenotypes involved in Alzheimer's Disease (AD) pathogenesis. This finding necessitates methods for the discovery of causal brain changes in AD that integrate Magnetic Resonance Imaging measures and genotypic data. However, existing approaches for causal inference in this setting, such as the univariate Imaging Wide Association Study (UV-IWAS), suffer from inconsistent effect estimation and inflated Type I errors in the presence of genetic pleiotropy, the phenomenon in which a variant affects multiple causal intermediate risk phenotypes.

A powerful and versatile colocalization test.

Transcriptome-wide association studies (TWAS and PrediXcan) have been increasingly applied to detect associations between genetically predicted gene expressions and GWAS traits, which may suggest, however do not completely determine, causal genes for GWAS traits, due to the likely violation of their imposed strong assumptions for causal inference. Testing colocalization moves it closer to establishing causal relationships: if a GWAS trait and a gene's expression share the same associated SNP, it may suggest a regulatory (and thus putative causal) role of the SNP mediated through the gene on the GWAS trait. Accordingly, it is of interest to develop and apply various colocalization testing approaches.

Significance Testing for Allelic Heterogeneity.

It is useful to detect allelic heterogeneity (AH), , the presence of multiple causal SNPs in a locus, which, for example, may guide the development of new methods for fine mapping and determine how to interpret an appearing epistasis. In contrast to Mendelian traits, the existence and extent of AH for complex traits had been largely unknown until Hormozdiari proposed a Bayesian method, called causal variants identification in associated regions (CAVIAR), and uncovered widespread AH in complex traits. However, there are several limitations with CAVIAR.

Improved Use of Small Reference Panels for Conditional and Joint Analysis with GWAS Summary Statistics.

Due to issues of practicality and confidentiality of genomic data sharing on a large scale, typically only meta- or mega-analyzed genome-wide association study (GWAS) summary data, not individual-level data, are publicly available. Reanalyses of such GWAS summary data for a wide range of applications have become more and more common and useful, which often require the use of an external reference panel with individual-level genotypic data to infer linkage disequilibrium (LD) among genetic variants. However, with a small sample size in only hundreds, as for the most popular 1000 Genomes Project European sample, estimation errors for LD are not negligible, leading to often dramatically increased numbers of false positives in subsequent analyses of GWAS summary data.

Meningococcal carriage within households in the African meningitis belt: A longitudinal pilot study.

Objectives: Carriers of Neisseria meningitidis are a key source of transmission. In the African meningitis belt, where risk of meningococcal disease is highest, a greater understanding of meningococcal carriage dynamics is needed.

Methods: We randomly selected an age-stratified sample of 400 residents from 116 households in Bamako, Mali, and collected pharyngeal swabs in May 2010.