Survival Analysis

Lin, Qianhan

doi:10.1007/978-94-007-0753-5_2927

Cited by 9 publications

(12 citation statements)

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“…The proportional hazards model used in this article specifies the transition rate as (see, for instance, Blossfeld and Rohwer, 2002, p. 229; Buis, 2006, p. 22; Jenkins, 2005, p. 28; Lin, 2003, p. 821): …”

Section: Methodsmentioning

confidence: 99%

Growth of milk production in German dairy farms: an empirical study based on event history analysis

Francksen

Hagemann

Latacz‐Lohmann

2012

Agricultural Economics

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This article investigates determinants of growth of milk production in German dairy farms with the use of event history analysis. This methodology enables the analysts to consider time as a proxy for not measurable effects on growth. The likelihood of a farm's moving from a nongrowth episode into a growth episode is estimated and the impact of various covariates on that likelihood is assessed. The analysis is based upon a balanced panel of annual farm accounts from 616 specialized dairy farms in Germany, covering the financial years 1995/1996-2008/2009. The results from event history analyses are presented for low and high growth rates. For both groups, it was found that the probability of entering a growth episode, defined as the event to be analyzed, increases over time, e.g., as a consequence of an increasing need to improve competitiveness. Moreover, several covariates, such as the share of subsidies in returns, farmer's age, and milk price, had a significant impact on growth in a farm's milk production. The analysis revealed, however, that the effect-direction of some explanatory variables differed between the two groups.

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Section: Methodsmentioning

confidence: 99%

Growth of milk production in German dairy farms: an empirical study based on event history analysis

Francksen

Hagemann

Latacz‐Lohmann

2012

Agricultural Economics

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“…Consequently, a frailty model can be used to extend the proportional hazards regression model in survival analysis. Similar to the formulation of the frailty model provided by Klein and Moeschberger (2003), we can write the hazard rate at time t in i th patient as h i t ð Þ ¼ h 0 t ð Þ exp aj i þ x 0 ij2 b 2 ; i ¼ 1; 2;:::; m; j ¼ 1; 2;:::; n i…”

Section: Methodsmentioning

confidence: 99%

“…The EM algorithm provides a means of maximizing complex likelihoods. Here we use EM algorithm described by Klein and Moeschberger (2003). In the E-step of the algorithm, the expected value of ' FULL is computed, given the current estimates of the parameters and the observable data.…”

Section: Methodsmentioning

confidence: 99%

How the mechanism of missing data on longitudinal biomarkers influences the survival analysis

2019

Communications in Statistics - Theory and Methods

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In this paper, we discuss how missing data affect to identify longitudinal biomarkers or surrogates for a time to event outcome with/ without considering the assumption of heterogenous baseline hazards among individuals. The comparison will be applied to the missing data under two missing mechanismmissing at random (MAR) and non-ignorable missing (NMAR). We use simulations to explore how missing mechanism affect the power to detect the association of a longitudinal biomarker and the survival time given the number of individuals, the number of time points per individual and the functional form of the random effects from the longitudinal biomarkers considering heterogeneous baseline hazards in individuals. The longitudinal biomarker will be assumed under two missing mechanismmissing at random (MAR) and non-ignorable missing (NMAR). The result shows that the method considering heterogeneous baseline hazards among individuals can has a performance well in the missing data under missing at random (MAR). The method considering heterogeneous baseline hazards among individuals has the better performance than the method without considering heterogeneous baseline hazards among individuals. The method with/without considering heterogeneous baseline hazards among individuals do not work well under nonignorable missing mechanism.

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“…An overview of all functions in the CoxPhLb package is presented in Table 1. In the following sections, we provide R codes that illustrate how to use the functions with the simulated data that are available in the CoxPhLb package and a real dataset, the Channing House data, which is publicly available in the KMsurv package (Klein et al, 2012). The provided R codes can be implemented after installing and loading the CoxPhLb package, which will automatically load the survival package.…”

Section: Implementation Of Coxphlbmentioning

confidence: 99%

CoxPhLb: An R Package for Analyzing Length Biased Data under Cox Model

Lee¹,

Zhou²,

Ning³

et al. 2020

The R Journal

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Data subject to length-biased sampling are frequently encountered in various applications including prevalent cohort studies and are considered as a special case of left-truncated data under the stationarity assumption. Many semiparametric regression methods have been proposed for lengthbiased data to model the association between covariates and the survival outcome of interest. In this paper, we present a brief review of the statistical methodologies established for the analysis of length-biased data under the Cox model, which is the most commonly adopted semiparametric model, and introduce an R package CoxPhLb that implements these methods. Specifically, the package includes features such as fitting the Cox model to explore covariate effects on survival times and checking the proportional hazards model assumptions and the stationarity assumption. We illustrate usage of the package with a simulated data example and a real dataset, the Channing House data, which are publicly available.

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Survival Analysis

Cited by 9 publications

References 0 publications

Growth of milk production in German dairy farms: an empirical study based on event history analysis

Growth of milk production in German dairy farms: an empirical study based on event history analysis

How the mechanism of missing data on longitudinal biomarkers influences the survival analysis

CoxPhLb: An R Package for Analyzing Length Biased Data under Cox Model

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