5,289 research outputs found
Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer
Readmission following discharge from an initial hospitalization is a key
marker of quality of health care in the United States. For the most part,
readmission has been used to study quality of care for patients with acute
health conditions, such as pneumonia and heart failure, with analyses typically
based on a logistic-Normal generalized linear mixed model. Applying this model
to the study readmission among patients with increasingly prevalent advanced
health conditions such as pancreatic cancer is problematic, however, because it
ignores death as a competing risk. A more appropriate analysis is to imbed such
studies within the semi-competing risks framework. To our knowledge, however,
no comprehensive statistical methods have been developed for cluster-correlated
semi-competing risks data. In this paper we propose a novel hierarchical
modeling framework for the analysis of cluster-correlated semi-competing risks
data. The framework permits parametric or non-parametric specifications for a
range of model components, including baseline hazard functions and
distributions for key random effects, giving analysts substantial flexibility
as they consider their own analyses. Estimation and inference is performed
within the Bayesian paradigm since it facilitates the straightforward
characterization of (posterior) uncertainty for all model parameters including
hospital-specific random effects. The proposed framework is used to study the
risk of readmission among 5,298 Medicare beneficiaries diagnosed with
pancreatic cancer at 112 hospitals in the six New England states between
2000-2009, specifically to investigate the role of patient-level risk factors
and to characterize variation in risk across hospitals that is not explained by
differences in patient case-mix
A review on competing risks methods for survival analysis
When modelling competing risks survival data, several techniques have been
proposed in both the statistical and machine learning literature.
State-of-the-art methods have extended classical approaches with more flexible
assumptions that can improve predictive performance, allow high dimensional
data and missing values, among others. Despite this, modern approaches have not
been widely employed in applied settings. This article aims to aid the uptake
of such methods by providing a condensed compendium of competing risks survival
methods with a unified notation and interpretation across approaches. We
highlight available software and, when possible, demonstrate their usage via
reproducible R vignettes. Moreover, we discuss two major concerns that can
affect benchmark studies in this context: the choice of performance metrics and
reproducibility.Comment: 22 pages, 2 table
Unobserved Heterogeneity in Multiple-Spell Multiple-States Duration Models
In survival analysis a large literature using frailty models, or models with unobserved heterogeneity, exist. In the growing literate on multiple spell multiple states duration models, or multistate models, modeling this issue is only at its infant phase. Ignoring unobserved heteogeneity can, however, produce incorrect results. This paper presents how unobserved heterogeneity can be incorporated into multistate models, with an emphasis on semi-Markov multistate models with a mixed proportional hazard structure. First, the aspects of frailty modeling in univariate (proportional hazard, Cox) duration models are addressed and some important models with unobserved heterogeneity are discussed. Second, the domain is extended to modeling of parallel/clustered multivariate duration data with unobserved heterogeneity. The implications of choosing shared or correlated unobserved heterogeneity is highlighted. The relevant differences with recurrent events data is covered next. They include the choice of the time scale and risk set which both have important implications for the way unobserved heterogeneity influence the model. Multistate duration models can have both parallel and recurrent events. Incorporating unobserved heterogeneity in multistate models, therefore, brings all the previously addressed issues together. Although some estimation procedures are covered the emphasis is on conceptual issues. The importance of including unobserved heterogeneity in multistate duration models is illustrated with data on labour market and migration dynamics of recent immigrants to The Netherlands.multiple spell multiple state duration, mixed proportional hazard, multistate model, unobserved heterogeneity, frailty
The Multivariate Bernoulli detector: Change point estimation in discrete survival analysis
Time-to-event data are often recorded on a discrete scale with multiple,
competing risks as potential causes for the event. In this context, application
of continuous survival analysis methods with a single risk suffer from biased
estimation. Therefore, we propose the Multivariate Bernoulli detector for
competing risks with discrete times involving a multivariate change point model
on the cause-specific baseline hazards. Through the prior on the number of
change points and their location, we impose dependence between change points
across risks, as well as allowing for data-driven learning of their number.
Then, conditionally on these change points, a Multivariate Bernoulli prior is
used to infer which risks are involved. Focus of posterior inference is
cause-specific hazard rates and dependence across risks. Such dependence is
often present due to subject-specific changes across time that affect all
risks. Full posterior inference is performed through a tailored local-global
Markov chain Monte Carlo (MCMC) algorithm, which exploits a data augmentation
trick and MCMC updates from non-conjugate Bayesian nonparametric methods. We
illustrate our model in simulations and on prostate cancer data, comparing its
performance with existing approaches.Comment: 43 pages, 12 figure
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