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Lifetime Data Analysis

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https://www.readbyqxmd.com/read/30218418/defining-causal-meditation-with-a-longitudinal-mediator-and-a-survival-outcome
#1
Vanessa Didelez
In the context of causal mediation analysis, prevailing notions of direct and indirect effects are based on nested counterfactuals. These can be problematic regarding interpretation and identifiability especially when the mediator is a time-dependent process and the outcome is survival or, more generally, a time-to-event outcome. We propose and discuss an alternative definition of mediated effects that does not suffer from these problems, and is more transparent than the current alternatives. Our proposal is based on the extended graphical approach of Robins and Richardson (in: Shrout (ed) Causality and psychopathology: finding the determinants of disorders and their cures, Oxford University Press, Oxford, 2011), where treatment is decomposed into different components, or aspects, along different causal paths corresponding to real world mechanisms...
September 14, 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/30218417/what-price-semiparametric-cox-regression
#2
Martin Jullum, Nils Lid Hjort
Cox's proportional hazards regression model is the standard method for modelling censored life-time data with covariates. In its standard form, this method relies on a semiparametric proportional hazards structure, leaving the baseline unspecified. Naturally, specifying a parametric model also for the baseline hazard, leading to fully parametric Cox models, will be more efficient when the parametric model is correct, or close to correct. The aim of this paper is two-fold. (a) We compare parametric and semiparametric models in terms of their asymptotic relative efficiencies when estimating different quantities...
September 14, 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/30194655/proportional-cross-ratio-model
#3
Tianle Hu, Bin Nan, Xihong Lin
Cross-ratio is an important local measure of the strength of dependence among correlated failure times. If a covariate is available, it may be of scientific interest to understand how the cross-ratio varies with the covariate as well as time components. Motivated by the Tremin study, where the dependence between age at a marker event reflecting early lengthening of menstrual cycles and age at menopause may be affected by age at menarche, we propose a proportional cross-ratio model through a baseline cross-ratio function and a multiplicative covariate effect...
September 7, 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/30151802/hidden-three-state-survival-model-for-bivariate-longitudinal-count-data
#4
Ardo van den Hout, Graciela Muniz-Terrera
A model is presented that describes bivariate longitudinal count data by conditioning on a progressive illness-death process where the two living states are latent. The illness-death process is modelled in continuous time, and the count data are described by a bivariate extension of the binomial distribution. The bivariate distributions for the count data approach include the correlation between two responses even after conditioning on the state. An illustrative data analysis is discussed, where the bivariate data consist of scores on two cognitive tests, and the latent states represent two stages of underlying cognitive function...
August 27, 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/30076511/parametric-estimation-of-association-in-bivariate-failure-time-data-subject-to-competing-risks-sensitivity-to-underlying-assumptions
#5
Jeongyong Kim, Karen Bandeen-Roche
There has arisen a considerable body of research addressing the estimation of association between paired failure times in the presence of competing risks. In a 2002 paper, Bandeen-Roche and Liang proposed the conditional cause-specific hazard ratio (CCSHR) as a measure of this association and a parametric method by which to estimate it. The method features an interpretable decomposition of the CCSHR into factors describing the association between a pair's times to first failure among multiple failure causes and the association in pair members' propensities to fail due to a common cause...
August 3, 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/30014201/semiparametric-sieve-maximum-likelihood-estimation-under-cure-model-with-partly-interval-censored-and-left-truncated-data-for-application-to-spontaneous-abortion
#6
Yuan Wu, Christina D Chambers, Ronghui Xu
This work was motivated by observational studies in pregnancy with spontaneous abortion (SAB) as outcome. Clearly some women experience the SAB event but the rest do not. In addition, the data are left truncated due to the way pregnant women are recruited into these studies. For those women who do experience SAB, their exact event times are sometimes unknown. Finally, a small percentage of the women are lost to follow-up during their pregnancy. All these give rise to data that are left truncated, partly interval and right-censored, and with a clearly defined cured portion...
July 16, 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/30008054/discussion-of-survival-models-and-health-sequences-by-walter-dempsey-and-peter-mccullagh
#7
John D Kalbfleisch
This is a discussion of the paper by Dempsey and McCullagh.
July 14, 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/29995260/survival-models-and-health-sequences-discussion
#8
David Oakes
No abstract text is available yet for this article.
July 11, 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/29978275/testing-for-center-effects-on-survival-and-competing-risks-outcomes-using-pseudo-value-regression
#9
Yanzhi Wang, Brent R Logan
In multi-center studies, the presence of a cluster effect leads to correlation among outcomes within a center and requires different techniques to handle such correlation. Testing for a cluster effect can serve as a pre-screening step to help guide the researcher towards the appropriate analysis. With time to event data, score tests have been proposed which test for the presence of a center effect on the hazard function. However, sometimes researchers are interested in directly modeling other quantities such as survival probabilities or cumulative incidence at a fixed time...
July 5, 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/30083977/commentary-alignment-of-time-scales-and-joint-models
#10
Kwun Chuen Gary Chan
No abstract text is available yet for this article.
October 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/30076510/response-to-discussants-of-survival-models-and-health-sequences
#11
Walter Dempsey, Peter McCullagh
Survival studies often generate not only a survival time for each patient but also a sequence of health measurements at annual or semi-annual check-ups while the patient remains alive. Such a sequence of random length accompanied by a survival time is called a survival process. Robust health is ordinarily associated with longer survival, so the two parts of a survival process cannot be assumed independent. This paper is concerned with a general technique-reverse alignment-for constructing statistical models for survival processes, here termed revival models...
October 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/30022323/contribution-to-the-discussion-of-survival-models-and-health-sequences-by-w-dempsey-and-p-mccullagh
#12
Per Kragh Andersen
No abstract text is available yet for this article.
October 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/30022322/commentary-to-the-paper-by-walter-dempsey-and-peter-mccullagh
#13
Hans C van Houwelingen
No abstract text is available yet for this article.
October 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/29961227/editorial-to-accompany-the-discussion-paper-survival-models-and-health-sequences-by-walter-dempsey-and-peter-mccullagh
#14
EDITORIAL
Niels Keiding
No abstract text is available yet for this article.
October 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/29502184/survival-models-and-health-sequences
#15
Walter Dempsey, Peter McCullagh
Survival studies often generate not only a survival time for each patient but also a sequence of health measurements at annual or semi-annual check-ups while the patient remains alive. Such a sequence of random length accompanied by a survival time is called a survival process. Robust health is ordinarily associated with longer survival, so the two parts of a survival process cannot be assumed independent. This paper is concerned with a general technique-reverse alignment-for constructing statistical models for survival processes, here termed revival models...
October 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/29374340/illness-death-model-statistical-perspective-and-differential-equations
#16
Ralph Brinks, Annika Hoyer
The aim of this work is to relate the theory of stochastic processes with the differential equations associated with multistate (compartment) models. We show that the Kolmogorov Forward Differential Equations can be used to derive a relation between the prevalence and the transition rates in the illness-death model. Then, we prove mathematical well-definedness and epidemiological meaningfulness of the prevalence of the disease. As an application, we derive the incidence of diabetes from a series of cross-sections...
October 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/29238894/a-nonparametric-maximum-likelihood-approach-for-survival-data-with-observed-cured-subjects-left-truncation-and-right-censoring
#17
Jue Hou, Christina D Chambers, Ronghui Xu
We consider observational studies in pregnancy where the outcome of interest is spontaneous abortion (SAB). This at first sight is a binary 'yes' or 'no' variable, albeit there is left truncation as well as right-censoring in the data. Women who do not experience SAB by gestational week 20 are 'cured' from SAB by definition, that is, they are no longer at risk. Our data is different from the common cure data in the literature, where the cured subjects are always right-censored and not actually observed to be cured...
October 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/29185212/a-semiparametric-additive-rate-model-for-a-modulated-renewal-process
#18
Xin Chen, Jieli Ding, Liuquan Sun
Recurrent event data from a long single realization are widely encountered in point process applications. Modeling and analyzing such data are different from those for independent and identical short sequences, and the development of statistical methods requires careful consideration of the underlying dependence structure of the long single sequence. In this paper, we propose a semiparametric additive rate model for a modulated renewal process, and develop an estimating equation approach for the model parameters...
October 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/29170932/estimating-cross-quantile-residual-ratio-with-left-truncated-semi-competing-risks-data
#19
Jing Yang, Limin Peng
A semi-competing risks setting often arises in biomedical studies, involving both a nonterminal event and a terminal event. Cross quantile residual ratio (Yang and Peng in Biometrics 72:770-779, 2016) offers a flexible and robust perspective to study the dependency between the nonterminal and the terminal events which can shed useful scientific insight. In this paper, we propose a new nonparametric estimator of this dependence measure with left truncated semi-competing risks data. The new estimator overcomes the limitation of the existing estimator that is resulted from demanding a strong assumption on the truncation mechanism...
October 2018: Lifetime Data Analysis
https://www.readbyqxmd.com/read/29098489/investigating-the-correlation-structure-of-quadrivariate-udder-infection-times-through-hierarchical-archimedean-copulas
#20
Leen Prenen, Roel Braekers, Luc Duchateau
The correlation structure imposed on multivariate time to event data is often of a simple nature, such as in the shared frailty model where pairwise correlations between event times in a cluster are all the same. In modeling the infection times of the four udder quarters clustered within the cow, more complex correlation structures are possibly required, and if so, such more complex correlation structures give more insight in the infection process. In this article, we will choose a marginal approach to study more complex correlation structures, therefore leaving the modeling of marginal distributions unaffected by the association parameters...
October 2018: Lifetime Data Analysis
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