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Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 2020 Introduction The frequently used Cox regression applies two critical assumptions, which might not hold for all predictors. In this study, the results from a Cox regression model (CM) and a generalized Cox regression model (GCM) are compared. Methods Data
Jantje Goerdten +2 more
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Partial-linear single-index Cox regression models with multiple time-dependent covariates [PDF]
BMC Medical Research MethodologyBackground In cohort studies with time-to-event outcomes, covariates of interest often have values that change over time. The classical Cox regression model can handle time-dependent covariates but assumes linear effects on the log hazard function, which
Myeonggyun Lee +8 more
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Cox Point Process Regression [PDF]
IEEE Transactions on Information Theory, 2022 Point processes in time have a wide range of applications that include the claims arrival process in insurance or the analysis of queues in operations research. Due to advances in technology, such samples of point processes are increasingly encountered. A key object of interest is the local intensity function.
Álvaro Gajardo, Hans-Georg Müller
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Cox regression with linked data [PDF]
Statistics in Medicine, 2023 Record linkage is increasingly used, especially in medical studies, to combine data from different databases that refer to the same entities. The linked data can bring analysts novel and valuable knowledge that is impossible to obtain from a single database.
Vo, Thanh Huan +6 more
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Features of using Cox regression in various instrumental environments
Вестник университета, 2022 The presence of large amounts of data in information and analytical systems makes it necessary to study them using machine learning and artificial intelligence methods.
I. V. Kramarenko, L. A. Konstantinova
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Assumption-Lean Cox Regression
Journal of the American Statistical Association, 2022 Inference for the conditional association between an exposure and a time-to-event endpoint, given covariates, is routinely based on partial likelihood estimators for hazard ratios indexing Cox proportional hazards models. This approach is flexible and makes testing straightforward, but is nonetheless not entirely satisfactory.
Stijn Vansteelandt +3 more
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Sinusoidal Cox Regression—A Rare Cancer Example
Cancer Informatics, 2010 Evidence of an association between survival time and date of birth would suggest an etiologic role for a seasonally variable environmental exposure occurring within a narrow perinatal time period.
Jimmy Thomas Efird
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Gene signature for prognosis in comparison of pancreatic cancer patients with diabetes and non-diabetes [PDF]
PeerJ, 2020 Background Pancreatic cancer (PC) has much weaker prognosis, which can be divided into diabetes and non-diabetes. PC patients with diabetes mellitus will have more opportunities for physical examination due to diabetes, while pancreatic cancer patients ...
Mingjun Yang +5 more
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An exponential bound for Cox regression [PDF]
Statistics & Probability Letters, 2012 We present an asymptotic exponential bound for the deviation of the survival function estimator of the Cox model. We show that the bound holds even when the proportional hazards assumption does not hold.
Goldberg, Y., Kosorok, M. R.
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Image Based Data Mining Using Per-voxel Cox Regression
Frontiers in Oncology, 2020 Image Based Data Mining (IBDM) is a novel analysis technique allowing the interrogation of large amounts of routine radiotherapy data. Using this technique, unexpected correlations have been identified between dose close to the prostate and biochemical ...
Andrew Green +11 more
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