Negera Wakgari DERESA - Academia.edu (original) (raw)

Papers by Negera Wakgari DERESA

Research paper thumbnail of Copula based Cox proportional hazards models for dependent censoring

Journal of the American Statistical Association

Research paper thumbnail of Correction to: ‘On semiparametric modelling, estimation and inference for survival data subject to dependent censoring’

Biometrika, 2021

The proof of Theorem 1 contained an error. This proof has therefore been removed from the text of... more The proof of Theorem 1 contained an error. This proof has therefore been removed from the text of the article online. The corrected proof is now given in the Supplementary Material.

Research paper thumbnail of On semiparametric modelling, estimation and inference for survival data subject to dependent censoring

Biometrika, 2020

Summary When modelling survival data, it is common to assume that the survival time TTT is condit... more Summary When modelling survival data, it is common to assume that the survival time TTT is conditionally independent of the censoring time CCC given a set of covariates. However, there are numerous situations in which this assumption is not realistic. The goal of this paper is therefore to develop a semiparametric normal transformation model which assumes that, after a proper nonparametric monotone transformation, the vector (T,C)(T, C)(T,C) follows a linear model, and the vector of errors in this bivariate linear model follows a standard bivariate normal distribution with a possibly nondiagonal covariance matrix. We show that this semiparametric model is identifiable, and propose estimators of the nonparametric transformation, the regression coefficients and the correlation between the error terms. It is shown that the estimators of the model parameters and the transformation are consistent and asymptotically normal. We also assess the finite-sample performance of the proposed method by co...

Research paper thumbnail of A multivariate normal regression model for survival data subject to different types of dependent censoring

Computational Statistics & Data Analysis, 2019

In survival analysis observations are often right censored and this complicates considerably the ... more In survival analysis observations are often right censored and this complicates considerably the analysis of these data. Right censoring can have several underlying causes: administrative censoring, loss to follow up, competing risks, etc. The (latent) censoring times corresponding to the latter two types of censoring are possibly related to the survival time of interest, and in that case this should be taken into account in the model. A unifying model is presented that allows these censoring mechanisms in one single model, and that is also able to incorporate the effect of covariates on these times. Each time variable is modelled by means of a transformed linear model, with the particularity that the error terms of the transformed times follow a multivariate normal distribution allowing for non-zero correlations. It is shown that the model is identified and the model parameters are estimated through a maximum likelihood approach. The performance of the proposed method is compared with methods that assume independent censoring using finite sample simulations. The results show that the proposed method exhibits major advantages in terms of reducing the bias of the parameter estimates. However, a strong deviation from normality and/or a strong violation of the homogeneous variance assumption may lead to biased estimates. Finally, the model and the estimation method are illustrated using the analysis of data coming from a prostate cancer clinical trial.

Research paper thumbnail of Flexible parametric model for survival data subject to dependent censoring

Biometrical Journal, 2019

When modeling survival data, it is common to assume that the (log-transformed) survival time (T) ... more When modeling survival data, it is common to assume that the (log-transformed) survival time (T) is conditionally independent of the (log-transformed) censoring time (C) given a set of covariates. There are numerous situations in which this assumption is not realistic, and a number of correction procedures have been developed for different models. However, in most cases, either some prior knowledge about the association between T and C is required, or some auxiliary information or data is supposed to be available. When this is not the case, the application of many existing methods turns out to be limited. The goal of this paper is to overcome this problem by developing a flexible parametric model, that is a type of transformed linear model. We show that the association between T and C is identifiable in this model. The performance of the proposed method is investigated both in an asymptotic way and through finite sample simulations. We also develop a formal goodness-of-fit test approach to assess the quality of the fitted model. Finally, the approach is applied to data coming from a study on liver transplants.

Research paper thumbnail of Copula based Cox proportional hazards models for dependent censoring

Journal of the American Statistical Association

Research paper thumbnail of Correction to: ‘On semiparametric modelling, estimation and inference for survival data subject to dependent censoring’

Biometrika, 2021

The proof of Theorem 1 contained an error. This proof has therefore been removed from the text of... more The proof of Theorem 1 contained an error. This proof has therefore been removed from the text of the article online. The corrected proof is now given in the Supplementary Material.

Research paper thumbnail of On semiparametric modelling, estimation and inference for survival data subject to dependent censoring

Biometrika, 2020

Summary When modelling survival data, it is common to assume that the survival time TTT is condit... more Summary When modelling survival data, it is common to assume that the survival time TTT is conditionally independent of the censoring time CCC given a set of covariates. However, there are numerous situations in which this assumption is not realistic. The goal of this paper is therefore to develop a semiparametric normal transformation model which assumes that, after a proper nonparametric monotone transformation, the vector (T,C)(T, C)(T,C) follows a linear model, and the vector of errors in this bivariate linear model follows a standard bivariate normal distribution with a possibly nondiagonal covariance matrix. We show that this semiparametric model is identifiable, and propose estimators of the nonparametric transformation, the regression coefficients and the correlation between the error terms. It is shown that the estimators of the model parameters and the transformation are consistent and asymptotically normal. We also assess the finite-sample performance of the proposed method by co...

Research paper thumbnail of A multivariate normal regression model for survival data subject to different types of dependent censoring

Computational Statistics & Data Analysis, 2019

In survival analysis observations are often right censored and this complicates considerably the ... more In survival analysis observations are often right censored and this complicates considerably the analysis of these data. Right censoring can have several underlying causes: administrative censoring, loss to follow up, competing risks, etc. The (latent) censoring times corresponding to the latter two types of censoring are possibly related to the survival time of interest, and in that case this should be taken into account in the model. A unifying model is presented that allows these censoring mechanisms in one single model, and that is also able to incorporate the effect of covariates on these times. Each time variable is modelled by means of a transformed linear model, with the particularity that the error terms of the transformed times follow a multivariate normal distribution allowing for non-zero correlations. It is shown that the model is identified and the model parameters are estimated through a maximum likelihood approach. The performance of the proposed method is compared with methods that assume independent censoring using finite sample simulations. The results show that the proposed method exhibits major advantages in terms of reducing the bias of the parameter estimates. However, a strong deviation from normality and/or a strong violation of the homogeneous variance assumption may lead to biased estimates. Finally, the model and the estimation method are illustrated using the analysis of data coming from a prostate cancer clinical trial.

Research paper thumbnail of Flexible parametric model for survival data subject to dependent censoring

Biometrical Journal, 2019

When modeling survival data, it is common to assume that the (log-transformed) survival time (T) ... more When modeling survival data, it is common to assume that the (log-transformed) survival time (T) is conditionally independent of the (log-transformed) censoring time (C) given a set of covariates. There are numerous situations in which this assumption is not realistic, and a number of correction procedures have been developed for different models. However, in most cases, either some prior knowledge about the association between T and C is required, or some auxiliary information or data is supposed to be available. When this is not the case, the application of many existing methods turns out to be limited. The goal of this paper is to overcome this problem by developing a flexible parametric model, that is a type of transformed linear model. We show that the association between T and C is identifiable in this model. The performance of the proposed method is investigated both in an asymptotic way and through finite sample simulations. We also develop a formal goodness-of-fit test approach to assess the quality of the fitted model. Finally, the approach is applied to data coming from a study on liver transplants.