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Genetic Analysis of Days Open in Moroccan Holstein Using Different Models to Account for Censored Data.
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December 2024
Laboratory of Biodiversity, Ecology and Genome, Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, B.P. 1014 RP, Rabat 10100, Morocco.
Reproductive efficiency is a key element of profitability in dairy herds. However, the genetic evaluation of fertility traits is often challenged by the presence of high censorship rates due to various reasons. An easy approach to address this challenge is to remove the censored data from the dataset.
View Article and Find Full Text PDFInformative censoring in externally controlled clinical trials: a potential source of bias.
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January 2025
Dana-Farber Cancer Institute, Boston, USA; Harvard School of Public Health, Boston, USA.
Background: Cancer researchers frequently consider the use of single-arm and randomized controlled clinical trial designs that leverage external data. The literature has reported extensively on how the use of external data can introduce bias through a variety of distortion mechanisms. In this article, we focus on a distortion mechanism that is often overlooked: informative censoring.
View Article and Find Full Text PDFConcordance-based Predictive Uncertainty (CPU)-Index: Proof-of-concept with application towards improved specificity of lung cancers on low dose screening CT.
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December 2024
Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America; Medical Physics Graduate Program, Duke University, Durham, NC, United States of America; Department of Radiology, Duke University, Durham, NC, United States of America; Department of Biomedical Engineering, Duke University, Durham, NC, United States of America; Department of Radiation Oncology, Duke University, Durham, NC, United States of America; Department of Pathology, Duke University, Durham, NC, United States of America. Electronic address:
In this paper, we introduce a novel concordance-based predictive uncertainty (CPU)-Index, which integrates insights from subgroup analysis and personalized AI time-to-event models. Through its application in refining lung cancer screening (LCS) predictions generated by an individualized AI time-to-event model trained with fused data of low dose CT (LDCT) radiomics with patient demographics, we demonstrate its effectiveness, resulting in improved risk assessment compared to the Lung CT Screening Reporting & Data System (Lung-RADS). Subgroup-based Lung-RADS faces challenges in representing individual variations and relies on a limited set of predefined characteristics, resulting in variable predictions.
View Article and Find Full Text PDFTime to Take a Chance: The Promise of Royston-Parmar Proportional Hazard Models for Understanding Caseload Transitions.
Eval Rev
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Division of Family Development, New Jersey Department of Human Services, Trenton, NJ, USA.
In this letter to the editor, we compare six different event history models to estimate eligible families participated in a subsidized rental housing program and . Answering these questions can inform efforts to improve program marketing and outreach, staffing and budgeting, triage, bias identification, as well as benchmarking and evaluation. One of six specifications clearly outperforms the others and understanding how will inform similar research pursuits.
View Article and Find Full Text PDFAre We Estimating the Mean and Variance Correctly in the Presence of Observations Outside of Measurable Range?
Pharmacol Res Perspect
February 2025
Slovak Academy of Sciences Institute of Neuroimmunology, Bratislava, Slovakia.
Laboratory measurements used for safety assessments in clinical trials are subject to the limits of the used laboratory equipment. These limits determine the range of values which the equipment can accurately measure. When observations fall outside the measurable range, this creates a problem in estimating parameters of the normal distribution.
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