Partial Likelihood Estimation of a Cox Model with Random Effects: an EM Algorithm based on Penalized Likelihood.

The aim of this paper is to present a general EM algorithm to estimate Mixed Proportional Hazard models including more than one random effect, through partial likelihood. We assume only that the mixing distributions admit Laplace transforms. We show how to transform inference in a single complicated model in the estimation of MPH models involving only a single frailty, which are easily manageable. We then face on gamma unobserved heterogeneity. This choice is a weak assumption as the heterogeneity distribution among survivors converges to a gamma distribution, often quickly, for many types of unobserved heterogeneity distributions. The proposed approach can thus be used to estimate a wide class of models. We describe how to use the penalized partial likelihood within the EM algorithm, to improve speed and stability. The behaviour of the estimator on different clusterings and sample sizes is assessed through a Monte Carlo study. We also provide an application on the ratiffcation of ILO conventions by developing countries over the period 1975-1995. Both the simulations and the empirical results indicate an important decrease in computing time. Furthermore, our procedure converges in settings where a standard EM algorithm does not.Random Effects, Duration analysis, Dynamic model

Identification of Lagged Duration Dependence in Multiple Spells Competing Risks Models

We show non-parametric identification of lagged duration dependence in mixed proportional hazard models for duration data, in the presence of competing risks and consecutive spells. We extend the results to the case in which data provide repeated realizations of consecutive spells competing risks structure for each subjectLagged duration dependence; competing risks, MPH models, identification

Inference in Mixed Proportional Hazard Models with K Random Effects

A general formulation of Mixed Proportional Hazard models with K random effects is provided. It enables to account for a population stratified at K different levels. We then show how to approximate the partial maximum likelihood estimator using an EM algorithm. In a Monte Carlo study, the behavior of the estimator is assessed and I provide an application to the ratification of ILO conventions. Compared to other procedures, the results indicate an important decrease in computing time, as well as improved convergence and stability.EM algorithm, penalized likelihood, partial likelihood, frailties.

Heterogeneite non observee dans les modeles de duree

Cet article est une revue de la littérature où le temps passé dans un état est une variable aléatoire issue d’un mélange continu de distributions. Elle s’est constituée à partir de l’estimation de fonctions de hasards et de méthodes d’approximations d’intégrales. Nous présentons d’abord le modèle de mélange de hasards proprotionnels et ses propriétés. Les conséquences des principaux résultats d’identification sont ensuite discutées. Nous présentons ensuite des méthodes d’estimations paramétriques, semi-paramétriques et bayésiennes, ainsi que les méthodes d’optimisation correspondantes.modèles de durée, hasards proportionnels, vraisemblance pénalisée, vraisemblance partielle, méthodes bayésiennes

Capital Utilisation and Retirement

This empirical analysis aims at assessing the effect of the economic climate and the intensity of capital utilisation on companies' capital retirement behaviour. It is conducted using individual company data, as well as original data on the degree of utilisation of production factors. The sample includes 6,998 observations over the period 1996-2008. This database is, to our knowledge, unique for the empirical analysis of the intensity of capital utilisation on firms' capital retirement behaviour. We adjust for endogeneity biases by means of instrumental variables. The main results obtained from the estimation of capital retirement models may be summarised as follows: i) The retirement rate decreases with the variations in cyclical pressures measured by the changes in output and the workweek of capital; this relation corresponds to a countercyclical decelerator effect on capital retirement; ii) The capital retirement rate increases with the structural intensity of capital utilisation; this effect, which corresponds to a wear and tear one, is nevertheless small compared to the decelerator one; iii) The profit rate does not have a significant impact on the retirement rate. Compared with the existing literature, here mainly Mairesse and Dormont (1985), the contribution of these results is to show, through the use of unique survey data, that the effect of the intensity of capital utilisation on capital retirement is structurally positive, via a wear and tear effect, and cyclically negative, via a decelerator effect which completes that already taken into account via the effect of changes in value added.Capital; Capital measure; Capital retirement; Capital utilisation

Job durations with worker and firm specific effects: MCMC estimation with longitudinal employer-employee data

We study job durations using a multivariate hazard model allowing for workerspecific and firm-specific unobserved determinants. The latter are captured by unobserved heterogeneity terms or random effects, one at the firm level and another at the worker level. This enables us to decompose the variation in job durations into the relative contribution of the worker and the firm. We also allow the unobserved terms to be correlated. For the empirical analysis we use a Portuguese longitudinal matched employer-employee data set. The model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) estimation method. The results imply that firm characteristics explain around 30% of the variation in log job durations. In addition, we find a positive correlation between unobserved worker and firm characteristics.Job transitions; assortative matching; Gibbs sampling; frailties; dynamic models; matched employer-employee data

Job Durations with Worker and Firm Specific Effects: MCMC Estimation with Longitudinal Employer-Employee Data

We study job durations using a multivariate hazard model allowing for worker-specific and firm-specific unobserved determinants. The latter are captured by unobserved heterogeneity terms or random effects, one at the firm level and another at the worker level. This enables us to decompose the variation in job durations into the relative contribution of the worker and the firm. We also allow the unobserved terms to be correlated. For the empirical analysis we use a Portuguese longitudinal matched employer-employee data set. The model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) estimation method. The results imply that firm characteristics explain around 30% of the variation in log job durations. In addition, we find a positive correlation between unobserved worker and firm characteristics.job transitions, assortative matching, Gibbs sampling, frailties, dynamic models, matched employer-employee data

Capital Utilisation and Retirement

This empirical analysis aims at assessing the effect of the economic climate and the intensity of capital utilisation on companies' capital retirement behaviour. It is conducted using individual company data, as well as original data on the degree of utilisation of production factors. The sample includes 6,998 observations over the period 1996-2008. This database is, to our knowledge, unique for the empirical analysis of the intensity of capital utilisation on firms' capital retirement behaviour. We adjust for endogeneity biases by means of instrumental variables. The main results obtained from the estimation of capital retirement models may be summarised as follows: i) The retirement rate decreases with the variations in cyclical pressures measured by the changes in output and the workweek of capital; this relation corresponds to a countercyclical decelerator effect on capital retirement; ii) The capital retirement rate increases with the structural intensity of capital utilisation; this effect, which corresponds to a wear and tear one, is nevertheless small compared to the decelerator one; iii) The profit rate does not have a significant impact on the retirement rate. Compared with the existing literature, here mainly Mairesse and Dormont (1985), the contribution of these results is to show, through the use of unique survey data, that the effect of the intensity of capital utilisation on capital retirement is structurally positive, via a wear and tear effect, and cyclically negative, via a decelerator effect which completes that already taken into account via the effect of changes in value added

Bayesian Estimation of Cox Models With Non-Nested Random Effects: An Application to the Ratification of ILO Conventions by Developing Countries

We use a multivariate hazard model for the analysis of data on the timing of ratifications of different ILO conventions by developing countries. The model accounts for two random effects, one at the country level and the other at the convention level. After investigating identification. we use a semi-parametric Bayesian approach based on the partial likelihood for the inference. Our findings confirm the results of preceding studies that ratification depends both on economic and political factors. Furthermore, the results yield insights on the impact of unobserved heterogeneity across member states and conventions on the ratification process

Standards of genetic testing in the diagnosis and prognostication of systemic mastocytosis in 2022: Recommendations of the EU-US cooperative group

Mastocytosis comprises rare heterogeneous diseases characterized by an increased accumulation of abnormal mast cells in various organs/tissues. The pathogenesis of mastocytosis is strongly linked to the presence of KIT-activating mutations. In systemic mastocytosis (SM), the most frequent mutation encountered is KIT p.D816V, whose presence constitutes one of the minor diagnostic criteria. Different techniques are used to search and quantify the KIT p.D816V mutant; however, allele-specific quantitative PCR and droplet digital PCR are today the most sensitive. The analysis of the KIT p.D816V allele burden has undeniable interest for diagnostic, prognostic, and therapeutic monitoring. The analysis of non–mast cell hematological compartments in SM is similarly important because KIT p.D816V multilineage involvement is associated with a worse prognosis. In addition, in advanced forms of SM, mutations in genes other than KIT are frequently identified and affect negatively disease outcome and response to therapy. Thus, combined quantitative and sensitive analysis of KIT mutations and next-generation sequencing of other recurrently involved myeloid genes make it possible to better characterize the extent of the affected cellular compartments and additional molecular aberrations, providing a more detailed overview of the complex mutational landscape of SM, in relation with the clinical heterogeneity of the disease. In this article, we report the latest recommendations of the EU-US Cooperative Group presented in September 2020 in Vienna during an international working conference, on the techniques we consider standard to detect and quantify the KIT p.D816V mutant in SM and additional myeloid mutations found in SM subtypes.D.D.M., J.J.L., and M.C.C. were supported by the Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health. P.V. was supported by the Austrian Science Fund (FWF) (grant nos. F4704-B20 and P32470-B)