Results 241 to 250 of about 201,170 (293)
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Regression models for censored serological data
Journal of Medical Microbiology, 2013The impact was assessed of censored serological measurements on regression equations fitted to data from panels of sera tested by different laboratories, for the purpose of standardizing serosurvey results to common units. Several methods that adjust for censoring were compared, such as deletion, simple substitution, multiple imputation and censored ...
George, Kafatos +3 more
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Median Regression Model with Interval Censored Data
Biometrical Journal, 2010AbstractQuantile 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. +3 more
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Bayesian transformation models with partly interval‐censored data
Statistics in Medicine, 2021In many scientific fields, partly interval‐censored data, which consist of exactly observed and interval‐censored observations on the failure time of interest, appear frequently. However, methodological developments in the analysis of partly interval‐censored data are relatively limited and have mainly focused on additive or proportional hazards models.
Chunjie Wang +2 more
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Semi-Markov models for partially censored data
Biometrika, 1978SUMMARY Nonparametric likelihood methods are developed for the analysis of partially censored data arising from a multistate stochastic process. It is assumed that the underlying process follows a semi-Markov model in which state changes form an embedded Markov chain and sojourn times are independent with distributions depending only on adjoining ...
Lagakos, S. W., Sommer, C. J., Zelen, M.
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Cure rate model with interval censored data
Statistics in Medicine, 2007AbstractIn 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
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Analysis of Transformation Models with Censored Data
Biometrika, 1995Summary: We consider a class of semi-parametric transformation models, under which an unknown transformation of the survival time is linearly related to the covariates with various completely specified error distributions. This class of regression models includes the proportional hazards and proportional odds models. Inference procedures derived from a
Cheng, S. C., Wei, L. J., Ying, Z.
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Bayesian Neural Network Models for Censored Data
Biometrical Journal, 1997AbstractNeural networks are considered by many to be very promising tools for classification and prediction. The flexibility of the neural network models often result in over‐fit. Shrinking the parameters using a penalized likelihood is often used in order to overcome such over‐fit.
Faraggi, D. +3 more
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Estimation in Linear Models with Censored Data
Biometrika, 1986Summary: Three methods for linear regression with censored data are considered; that of \textit{J. Buckley} and \textit{I. James} [Biometrika 66, 429--436 (1979; Zbl 0425.62051)], a proposed simpler nonparametric method and a normal model for censored data.
Schneider, Helmut, Weissfeld, Lisa
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Fitting regression models to censored survival data
Statistics in Medicine, 1983AbstractA short review of regression models for the analysis of censored survival data is given. These include multiplicative hazard rate models, log‐linear models (accelerated failure time models), linear models and polynomial models. An application of some of these models to the analysis of a large retrospective study on carcinomas of the oral cavity
M, Hudec, H, Platz
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A random‐censoring Poisson model for underreported data
Statistics in Medicine, 2017A major challenge when monitoring risks in socially deprived areas of under developed countries is that economic, epidemiological, and social data are typically underreported. Thus, statistical models that do not take the data quality into account will produce biased estimates.
Guilherme Lopes de Oliveira +2 more
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