Results 191 to 200 of about 22,611 (308)

On a semiparametric survival model with flexible covariate effect.

open access: yes
A semiparametric hazard model with parametrized time but general covariate dependency is formulated and analyzed inside the framework of counting process theory. A profile likelihood principle is introduced for estimation of the parameters: the resulting
Nielsen, Jens P.   +2 more
core  

Clinicogenomic Features and Outcomes of Adenoid Cystic Carcinoma With Central Nervous System Metastases: A Single‐Institution Cohort Study

open access: yesHead &Neck, EarlyView.
ABSTRACT Background Adenoid cystic carcinoma (ACC) is a rare malignancy with a propensity for perineural invasion and hematogenous spread. Central nervous system (CNS) involvement is uncommon, and detailed clinical and genomic data on this aspect of the disease remain limited.
Omar Elghawy   +7 more
wiley   +1 more source

From Nodal Metastasis to Oncologic Recurrence in Tongue and Oral Floor Squamous Cell Carcinoma: Distinct Failure Patterns and Clinicopathological Risk Profiles

open access: yesHead &Neck, EarlyView.
ABSTRACT Background Locoregional recurrence remains a clinically relevant problem in oral squamous cell carcinoma (OSCC). Whether local, ipsilateral nodal, and contralateral nodal failure represent distinct disease processes with different clinicopathological associations has not been systematically evaluated.
Jannik Ketschau   +9 more
wiley   +1 more source

Informative censoring in piecewise exponential survival models

open access: yes
There are often reasons to suppose that there is dependence between the time to event and time to censoring, or informative censoring, for survival data, particularly when considering medical data.
Collett, David   +3 more
core  

RETRACTED: Polygenic risk score and prostate specific antigen predict death from prostate cancer in men with intermediate aggressive cancer

open access: yesInternational Journal of Cancer, EarlyView.
What's New? Using 21 SNPs, two novel PRS were constructed and used to develop two new machine‐learning classifiers, one for the detection of prostate cancer and the other for the prediction of its aggressiveness and subsequent mortality. The classifier for disease detection is built using the PRS as the sole feature, whereas the one for disease ...
Leandro Rodrigues Santiago   +3 more
wiley   +1 more source

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