The unobserved heterogeneity distribution in duration analysis

Abbring, Jaap H.; Berg, Gérard J. van den

doi:10.1093/biomet/asm013

Cited by 143 publications

(91 citation statements)

References 38 publications

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“…11 However, Abbring and van den Berg (2007) show that in duration models the heterogeneity distribution usually converges to a Gamma distribution.…”

Section: Model Specifications and Methods Of Estimationmentioning

confidence: 99%

“…This situation is comparable only to the other Mediterranean countries (Spain and Greece). The peculiarity of the Italian situation is stressed by the fact that even in countries where the general unemployment rate is close to the Italian one (France), the youth unemployment incidence is much lower (22%) 1 . Another important feature of Italian youth unemployment is that it is above all concentrated among women and in the South.…”

Section: Introductionmentioning

confidence: 99%

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The Transition to Work for Italian University Graduates

Pozzoli

2009

Labour

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This study investigates the hazard of first job for Italian graduates. The analysis is in particular focused on the transition from university to work, taking into account the graduates' characteristics and the effects relating to degree subject. It is used a large data set from a survey on job opportunities for the 1998 Italian graduates. The paper employs a non parametric discrete-time single risk models to study employment hazard. Alternative mixing distributions have also been used to account for unobserved heterogeneity. The results obtained indicate that there is evidence of positive duration dependence after a short initial period of negative duration dependence. In addition, competing risk model with unobserved heterogeneity and non parametric baseline hazard have been estimated to characterize transitions out of unemployment.JEL Classification: J64; C41; C50.

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“…11 However, Abbring and van den Berg (2007) show that in duration models the heterogeneity distribution usually converges to a Gamma distribution.…”

Section: Model Specifications and Methods Of Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Transition to Work for Italian University Graduates

Pozzoli

2009

Labour

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“…bias toward the null). This attenuation is known in the econometrics literature as the attenuation due to unobserved heterogeneity [28, 29], and has been discussed in the work by Gail et al [30]. …”

Section: Biased Estimates Of the Treatment Effect When Ignoring Thmentioning

confidence: 99%

Sample size and power determination in joint modeling of longitudinal and survival data

Chen

Ibrahim

Chu

2011

Statistics in Medicine

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Owing to the rapid development of biomarkers in clinical trials, joint modeling of longitudinal and survival data has gained its popularity in the recent years because it reduces bias and provides improvements of efficiency in the assessment of treatment effects and other prognostic factors. Although much effort has been put into inferential methods in joint modeling, such as estimation and hypothesis testing, design aspects have not been formally considered. Statistical design, such as sample size and power calculations, is a crucial first step in clinical trials. In this paper, we derive a closed-form sample size formula for estimating the effect of the longitudinal process in joint modeling, and extend Schoenfeld’s sample size formula to the joint modeling setting for estimating the overall treatment effect. The sample size formula we develop is quite general, allowing for p-degree polynomial trajectories. The robustness of our model is demonstrated in simulation studies with linear and quadratic trajectories. We discuss the impact of the within-subject variability on power and data collection strategies, such as spacing and frequency of repeated measurements, in order to maximize the power. When the within-subject variability is large, different data collection strategies can influence the power of the study in a significant way. Optimal frequency of repeated measurements also depends on the nature of the trajectory with higher polynomial trajectories and larger measurement error requiring more frequent measurements.

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“…This concept recognizes that population members may differ in their health and survival responses to the same stresses of life, and these differences together with their distribution in the population influence the shapes (age patterns) of observed (total) mortality rates. The heterogeneity variable may have biological, environmental, or other interpretations depending on the context, including study design and conditions (Abbring and Van den Berg 2007; Carnes and Olshansky 2001; Vaupel and Yashin 1985; Zajacova et al 2009). Vaupel and colleagues introduced important class of survival models with hidden heterogeneity, called “frailty models” (Vaupel et al 1979).…”

Section: Which Forces Shape the Age Patterns Of Human Mortality Ramentioning

confidence: 99%

How the effects of aging and stresses of life are integrated in mortality rates: insights for genetic studies of human health and longevity

et al. 2015

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Background Increasing proportions of elderly individuals in developed countries combined with substantial increases in related medical expenditures make the improvement of the health of the elderly a high priority today. If the process of aging by individuals is a major cause of age related health declines then postponing aging could be an efficient strategy for improving the health of the elderly. Implementing this strategy requires a better understanding of genetic and non-genetic connections among aging, health, and longevity. Data and methods We review progress and problems in research areas whose development may contribute to analyses of such connections. These include genetic studies of human aging and longevity, the heterogeneity of populations with respect to their susceptibility to disease and death, forces that shape age patterns of human mortality, secular trends in mortality decline, and integrative mortality modeling using longitudinal data. Results The dynamic involvement of genetic factors in (i) morbidity/mortality risks, (ii) responses to stresses of life, (iii) multi-morbidities of many elderly individuals, (iv) trade-offs for diseases, (v) genetic heterogeneity, and (vi) other relevant aging-related health declines, underscores the need for a comprehensive, integrated approach to analyze the genetic connections for all of the above aspects of aging-related changes. Conclusion The dynamic relationships among aging, health, and longevity traits would be better understood if one linked several research fields within one conceptual framework that allowed for efficient analyses of available longitudinal data using the wealth of available knowledge about aging, health, and longevity already accumulated in the research field.

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The unobserved heterogeneity distribution in duration analysis

Cited by 143 publications

References 38 publications

The Transition to Work for Italian University Graduates

The Transition to Work for Italian University Graduates

Sample size and power determination in joint modeling of longitudinal and survival data

How the effects of aging and stresses of life are integrated in mortality rates: insights for genetic studies of human health and longevity

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