Results 251 to 260 of about 783,235 (315)

Hazard Regression with Interval-Censored Data

Biometrics, 1997
In a recent paper, Kooperberg, Stone, and Truong (1995a) introduced hazard regression (HARE), in which linear splines and their tensor products are used to estimate the conditional log-hazard function based on possibly censored, positive response data and one or more covariates.
Kooperberg, Charles, Clarkson, Douglas B.   +1 more
openaire   +3 more sources

Regression analysis in an illness-death model with interval-censored data: A pseudo-value approach

Statistical Methods in Medical Research, 2020
Pseudo-values provide a method to perform regression analysis for complex quantities with right-censored data. A further complication, interval-censored data, appears when events such as dementia are studied in an epidemiological cohort.
Camille Sabathé, P. Andersen, C. Helmer, T. Gerds, H. Jacqmin-Gadda, P. Joly   +5 more
semanticscholar   +1 more source

Semiparametric Regression Analysis of Interval‐Censored Data

Biometrics, 2000
Summary.We propose a semiparametric approach to the proportional hazards regression analysis of interval‐censored data. An EM algorithm based on an approximate likelihood leads to an M‐step that involves maximizing a standard Cox partial likelihood to estimate regression coefficients and then using the Breslow estimator for the unknown baseline hazards.
Goetghebeur, Els, Ryan, Louise
openaire   +2 more sources

Median Regression Model with Interval Censored Data

Biometrical Journal, 2010
AbstractQuantile regression methods have been used to estimate upper and lower quantile reference curves as the function of several covariates. Especially, in survival analysis, median regression models to the right‐censored data are suggested with several assumptions.
Kim, Yang-J., Cho, HyungJun, Kim, Jinheum, Jhun, Myoungshic   +3 more
openaire   +2 more sources

Cure rate model with interval censored data

Statistics in Medicine, 2007
AbstractIn cancer trials, a significant fraction of patients can be cured, that is, the disease is completely eliminated, so that it never recurs. In general, treatments are developed to both increase the patients' chances of being cured and prolong the survival time among non‐cured patients.
Yang-Jin, Kim, Myoungshic, Jhun
openaire   +2 more sources

Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R, SAS, and BUGS

, 2017
K. Bogaerts, A. Komárek, E. Lesaffre
semanticscholar   +2 more sources

Quantile regression for interval censored data

Communications in Statistics - Theory and Methods, 2017
ABSTRACTAs direct generalization of the quantile regression for complete observed data, an estimation method for quantile regression models with interval censored data is proposed, and the property of consistency is obtained. The property of asymptotic normality is also established with a bias converging to zero, and to reduce the bias, two bias ...
Xiuqing Zhou, Yanqin Feng, Xiuli Du
semanticscholar   +2 more sources

Variable Selection for Interval‐censored Failure Time Data

International Statistical Review, 2021
SummaryVariable selection for interval‐censored failure time data has recently attracted a great deal of attention along with the analysis of interval‐censored data in both method developments and practical applications. Interval‐censored data are a general type of time‐to‐event or failure time data where the failure time of interest is known or ...
Mingyue Du, Jianguo Sun
openaire   +1 more source

A pairwise pseudo‐likelihood approach for left‐truncated and interval‐censored data under the Cox model

Biometrics, 2020
Left truncation commonly occurs in many areas, and many methods have been proposed in the literature for the analysis of various types of left‐truncated failure time data.
Peijie Wang, Danning Li, Jianguo Sun
semanticscholar   +1 more source

Penalized estimation of semiparametric transformation models with interval-censored data and application to Alzheimer’s disease

Statistical Methods in Medical Research, 2019
Variable selection or feature extraction is fundamental to identify important risk factors from a large number of covariates and has applications in many fields.
Shuwei Li, Qiwei Wu, Jianguo Sun
semanticscholar   +1 more source