Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges
Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk
models have been based on regression models. While useful and robust, these statistical …
models have been based on regression models. While useful and robust, these statistical …
Radiomic analysis: study design, statistical analysis, and other bias mitigation strategies
Rapid advances in automated methods for extracting large numbers of quantitative features
from medical images have led to tremendous growth of publications reporting on radiomic …
from medical images have led to tremendous growth of publications reporting on radiomic …
Sample size for binary logistic prediction models: beyond events per variable criteria
M van Smeden, KGM Moons… - … methods in medical …, 2019 - journals.sagepub.com
Binary logistic regression is one of the most frequently applied statistical approaches for
developing clinical prediction models. Developers of such models often rely on an Events …
developing clinical prediction models. Developers of such models often rely on an Events …
Trends, characteristics, and outcomes of placenta accreta spectrum: a national study in the United States
S Matsuzaki, RS Mandelbaum, RN Sangara… - American journal of …, 2021 - Elsevier
Background Although an infrequent occurrence, the placenta can adhere abnormally to the
gravid uterus leading to significantly high maternal morbidity and mortality during cesarean …
gravid uterus leading to significantly high maternal morbidity and mortality during cesarean …
Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases
In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms
that resist conventional antibiotic treatment has steadily increased. Thus, it is now …
that resist conventional antibiotic treatment has steadily increased. Thus, it is now …
Sample size considerations and predictive performance of multinomial logistic prediction models
VMT de Jong, MJC Eijkemans… - Statistics in …, 2019 - Wiley Online Library
Multinomial Logistic Regression (MLR) has been advocated for developing clinical
prediction models that distinguish between three or more unordered outcomes. We present …
prediction models that distinguish between three or more unordered outcomes. We present …
Poor performance of clinical prediction models: the harm of commonly applied methods
Objective To evaluate limitations of common statistical modeling approaches in deriving
clinical prediction models and explore alternative strategies. Study Design and Setting A …
clinical prediction models and explore alternative strategies. Study Design and Setting A …
Regression shrinkage methods for clinical prediction models do not guarantee improved performance: simulation study
B Van Calster, M van Smeden… - … methods in medical …, 2020 - journals.sagepub.com
When developing risk prediction models on datasets with limited sample size, shrinkage
methods are recommended. Earlier studies showed that shrinkage results in better …
methods are recommended. Earlier studies showed that shrinkage results in better …
Polygenic scores in cancer
Since the publication of the first genome-wide association study for cancer in 2007,
thousands of common alleles that are associated with the risk of cancer have been …
thousands of common alleles that are associated with the risk of cancer have been …
[HTML][HTML] Urinary trace metals individually and in mixtures in association with preterm birth
One in ten infants born in the United States is born preterm, or prior to 37 weeks gestation.
Exposure to elevated levels of metals, such as lead and arsenic, has been linked to higher …
Exposure to elevated levels of metals, such as lead and arsenic, has been linked to higher …