Additive hazards models for gap time data with multiple causes

Sankaran, P. G.; Anisha, P.

doi:10.1016/j.spl.2012.02.025

Cited by 10 publications

(4 citation statements)

References 19 publications

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“…Later, Martinussen and Scheike (2002) and Lu and Song (2012) used a semiparametric efficient score function to improve estimation efficiency, at the cost of estimating λ0false(tfalse) separately. Some of the recent literature that considered data arising from an AHM include bivariate current status data (Tong et al, 2012), current status data with auxiliary covariates (Feng et al, 2015), informative current status data (Zhao et al, 2015), clustered interval-censored data (Li et al, 2012), gap time data of recurrent events with multiple causes (Sankaran and Anisha, 2012), left-truncated and right-censored data (Huang and Qin, 2013), right-censored data with missing covariates (Hao et al, 2014), right-censored data with missing censoring indicator (Qiu et al, 2015), left-truncated and case I interval-censored data (Wang et al, 2015), right-censored data with instrumental variable (Li et al, 2015) and error-contaminated survival data with replicate measurements (Yan and Yi, 2016), among others.…”

Section: Introductionmentioning

confidence: 99%

Estimation of partly linear additive hazards model with left-truncated and right-censored data

Afzal

Dong

2017

Statistical Modelling

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In this article, we consider an additive hazards semiparametric model for left-truncated and right-censored data where the risk function has a partly linear structure, we call it the partly linear additive hazards model. The nonlinear components are assumed to be B-splines functions, so the model can be viewed as a semiparametric model with an unknown baseline hazard function and a partly linear parametric risk function, which can model both linear and nonlinear covariate effects, hence is more flexible than a purely linear or nonlinear model. We construct a pseudo-score function to estimate the coefficients of the linear covariates and the B-spline basis functions. The proposed estimators are asymptotically normal under the assumption that the true nonlinear functions are B-spline functions whose knot locations and number of knots are held fixed. On the other hand, when the risk functions are unknown non-parametric functions, the proposed method provides a practical solution to the underlying inference problems. We conduct simulation studies to empirically examine the finite-sample performance of the proposed method and analyze a real dataset for illustration.

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Section: Introductionmentioning

confidence: 99%

Estimation of partly linear additive hazards model with left-truncated and right-censored data

Afzal

Dong

2017

Statistical Modelling

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“…Many approaches for the analysis of gap times can be highlighted, and among them we can refer to literature. [24][25][26][27][28][29] In survival studies, covariates are usually used to represent heterogeneity in a population. The main goal in such situations is to understand and explore the relationship between survival time and covariates.…”

Section: Introductionmentioning

confidence: 99%

A gap time model based on a multiplicative marginal rate function that accounts for zero-recurrence units

Louzada

Macera

Cancho

2017

Stat Methods Med Res

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In this article, we propose an alternative gap time model based on a multiplicative marginal rate function, which is formulated considering each gap time conditional on the previous recurrence times. In this formulation, the gap times are treated equally and the relation between successive events is no longer a problem. Furthermore, this article considers the inclusion of a proportion of zero-recurrence units (for which the event of interest will not occur) into the model to analyze recurrent event data. Inference aspects of the proposed model are discussed through maximum likelihood approach. A simulation study is carried out to examine the performance of the estimation procedure. The model is applied to hospital readmission data among colorectal cancer patients.

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“…Other models, such as hidden Markov models studied by Pievatolo et al (2012) can also be considered as generalizations of renewal processes for gap times. Recently, Sankaran and Anisha (2012) proposed the additive hazard models for gap time with multiple causes, and Zhao and Zhou (2012) developed a marginal rate model for gap times, which is derived from a nonhomogeneous Poisson process. Several other approaches to analyzing recurrent events have been proposed, and many of these approaches can be found in Kalbfleisch and Prentice (2002), Cook and Lawless (2007), and Aalen et al (2008).…”

Section: Introductionmentioning

confidence: 99%

The exponential‐Poisson model for recurrent event data: An application to a set of data on malaria in Brazil

et al. 2014

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In this paper, we introduce a new model for recurrent event data characterized by a baseline rate function fully parametric, which is based on the exponential-Poisson distribution. The model arises from a latent competing risk scenario, in the sense that there is no information about which cause was responsible for the event occurrence. Then, the time of each recurrence is given by the minimum lifetime value among all latent causes. The new model has a particular case, which is the classical homogeneous Poisson process. The properties of the proposed model are discussed, including its hazard rate function, survival function, and ordinary moments. The inferential procedure is based on the maximum likelihood approach. We consider an important issue of model selection between the proposed model and its particular case by the likelihood ratio test and score test. Goodness of fit of the recurrent event models is assessed using Cox-Snell residuals. A simulation study evaluates the performance of the estimation procedure in the presence of a small and moderate sample sizes. Applications on two real data sets are provided to illustrate the proposed methodology. One of them, first analyzed by our team of researchers, considers the data concerning the recurrence of malaria, which is an infectious disease caused by a protozoan parasite that infects red blood cells.

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Additive hazards models for gap time data with multiple causes

Cited by 10 publications

References 19 publications

Estimation of partly linear additive hazards model with left-truncated and right-censored data

Estimation of partly linear additive hazards model with left-truncated and right-censored data

A gap time model based on a multiplicative marginal rate function that accounts for zero-recurrence units

The exponential‐Poisson model for recurrent event data: An application to a set of data on malaria in Brazil

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