A multistate joint model for interval‐censored event‐history data subject to within‐unit clustering and informative missingness, with application to neurocysticercosis research

2020 ◽  
Vol 39 (23) ◽  
pp. 3195-3206 ◽  
Author(s):  
Hongbin Zhang ◽  
Elizabeth A. Kelvin ◽  
Arturo Carpio ◽  
W. Allen Hauser
Keyword(s):  
Joint Model ◽  
Event History ◽  
History Data ◽  
Event History Data ◽  
Interval Censored ◽  
Data Subject

Event history data structures

2019 ◽  
pp. 41-62
Author(s):  
Hans-Peter Blossfeld ◽  
Götz Rohwer ◽  
Thorsten Schneider ◽  
Brendan Halpin
Keyword(s):  
Data Structures ◽  
Event History ◽  
History Data ◽  
Event History Data

Event History Data

2011 ◽  
pp. 415-494
Author(s):  
Ludwig Fahrmeir ◽  
Thomas Kneib
Keyword(s):  
Event History ◽  
History Data ◽  
Event History Data

Escaping welfare? Social assistance dynamics in Sweden

2011 ◽  
Vol 21 (5) ◽  
pp. 486-500 ◽  
Author(s):  
Olof Bäckman ◽  
Åke Bergmark
Keyword(s):  
Mixture Model ◽  
Unobserved Heterogeneity ◽  
Social Assistance ◽  
Temporal Patterns ◽  
Previous Experience ◽  
Event History ◽  
Duration Dependence ◽  
Swedish Population ◽  
History Data ◽  
Event History Data

The article analyses temporal patterns in social assistance receipt in Sweden in the 2000s by looking at which circumstances facilitate versus reduce the possibilities of a person ceasing to be a recipient of social assistance. The analysis is guided by the following questions: What conditions lead people to terminate periods of social assistance receipt? Which factors are central to exits with different subsequent income patterns? How do these explain the different situations of recipients prior to termination? We focus particularly on income maintenance prior to spells of social assistance. We use event history data on monthly social assistance take-up covering the total adult Swedish population for the years 2002–2004. We adopt a gamma mixture model to control for unobserved heterogeneity. The results suggest that previous experience of both employment and social assistance receipt are important determinants for all types of exits from social assistance recipiency. A negative duration dependence is found also when unobserved heterogeneity is controlled for.

A general approach to the machine handling of event history data

1985 ◽  
Vol 24 (1) ◽  
pp. 161-188 ◽  
Author(s):  
Máire Ní Bhrolcháin ◽  
Ian M. Timaeus
Keyword(s):  
Event History ◽  
History Data ◽  
Event History Data

A mixture of beta–Dirichlet processes prior for Bayesian analysis of event history data

2013 ◽  
Vol 42 (3) ◽  
pp. 313-321 ◽  
Author(s):  
Minwoo Chae ◽  
Rafael Weißbach ◽  
Kwang Hyun Cho ◽  
Yongdai Kim
Keyword(s):  
Bayesian Analysis ◽  
Event History ◽  
Dirichlet Processes ◽  
History Data ◽  
Event History Data

A segmented regression model for event history data: an application to the fertility patterns in Italy

2009 ◽  
Vol 36 (9) ◽  
pp. 973-988 ◽  
Author(s):  
Vito M.R. Muggeo ◽  
Massimo Attanasio ◽  
Mariano Porcu
Keyword(s):  
Regression Model ◽  
Event History ◽  
Segmented Regression ◽  
History Data ◽  
Event History Data ◽  
Segmented Regression Model

scholarly journals Semiparametric analysis of complex longitudinal data

2020 ◽  
Author(s):  
◽  
Dayu Sun
Keyword(s):  
Variable Selection ◽  
Longitudinal Data ◽  
Recurrent Event ◽  
Event History ◽  
Panel Count Data ◽  
Recurrent Event Data ◽  
Response Variable ◽  
History Data ◽  
Event History Data ◽  
Observation Process

Event history data consist of the longitudinal records of event occurrence times. Recurrent event data and panel count data are two common types of event history data that occur in many areas, such as medical studies and social sciences. A great deal of literature has been established for their analyses. Nevertheless, only limited research exists on the variable selection for recurrent event data and panel count data. The existing methods can be seen as direct generalizations of the available penalized procedures for linear models, but may not perform as well as expected due to the complex structure of event history data. The first and second parts of this dissertation then discuss simultaneous parameter estimation and variable selection for event history data. We present a new variable selection method with a new penalty function, which will be referred to as the broken adaptive ridge regression approach. In addition to the establishment of the oracle property, we also show that the proposed variable selection method has the clustering or grouping effect when covariates are highly correlated. Furthermore, the numerical studies are performed and indicate that the method works well for practical situations and can outperform the existing methods. Applications to real data are provided. Most of the existing studies of longitudinal data assume that covariates can be observed at the same observation times for the response variable, and the observation process is independent of the response variable completely or given covariates. In practice, the response variables and covariates are sometimes observed intermittently at different time points, leading to sparse asynchronous longitudinal data. The observation process may also be related to the response variable even given covariates and sometimes both issues can even occur at the same time. Although each of the two issues has been developed to address in literature, it does not seem to exist an established approach that can deal with both together. To address both issues simultaneously, the third part of this dissertation proposes a flexible semiparametric transformation conditional model and a kernel-weighted estimating equation based approach. The proposed estimators of regression parameters are shown to be consistent and asymptotically follow the normal distribution. For the assessment of the finite sample performance of the proposed method, an extensive simulation study is carried out and suggests that it performs well for practical situations. The approach is applied to a prospective HIV study that motivated this investigation.

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