Score tests based on a finite mixture model of Markov processes under intermittent observation

Jiang, Shu; Cook, Richard J.

doi:10.1002/sim.8155

Cited by 6 publications

(6 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the g-mixture, the regime probabilities depend only on an initial state, while the Markov processes have their own intensity matrices. We observe g-mixture continuously, whereas Jiang and Cook (2019) observe the mixture intermittently.…”

Section: Introductionmentioning

confidence: 76%

See 1 more Smart Citation

Statistical inference for a mixture of Markov jump processes

Frydman¹,

Surya²

2021

Preprint

View full text Add to dashboard Cite

We estimate a general mixture of Markov jump processes. The key novel feature of the proposed mixture is that the transition intensity matrices of the Markov processes comprising the mixture are entirely unconstrained. The Markov processes are mixed with distributions that depend on the initial state of the mixture process. The new mixture is estimated from its continuously observed realizations using the EM algorithm, which provides the maximum likelihood (ML) estimates of the mixture's parameters. To obtain the standard errors of the estimates of the mixture's parameters, we employ Louis' (1982) general formula for the observed Fisher information matrix. We also derive the asymptotic properties of the ML estimators. Simulation study verifies the estimates' accuracy and confirms the consistency and asymptotic normality of the estimators. The developed methods are applied to a medical dataset, for which the likelihood ratio test rejects the constrained mixture in favor of the proposed unconstrained one. This application exemplifies the usefulness of a new unconstrained mixture for identification and characterization of homogeneous subpopulations in a heterogeneous population.

show abstract

Section: Introductionmentioning

confidence: 76%

“…The g-mixture is different from the mixture of Markov processes recently considered by Jiang and Cook (2019). There, the regime probabilities depend on covariates through the multinomial logistic regression, while the Markov processes in the mixture are assumed to have the same intensity matrices.…”

Section: Introductionmentioning

confidence: 99%

Statistical inference for a mixture of Markov jump processes

Frydman¹,

Surya²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Complex disease processes often feature heterogeneity beyond that explained through covariates. Jiang and Cook 32 describe finite mixture models of multistate processes under intermittent observation and develop score tests for effects of biomarkers on class membership. Here the use of score tests was motivated by the need to screen a large number of genetic markers for their association with the disease course combined with the difficulty in fitting such mixture models.…”

Section: Discussionmentioning

confidence: 99%

“…Once a list of candidate genetic markers are identified by this approach, it is natural to incorporate them into a predictive model for the disease course. In this case, one could model covariate effects on class membership as done in Jiang and Cook, 32 as well as on the intensity functions of the multistate process in the different classes. Such a rich predictive model could then be used to predict state occupancy at

{t}_0

—our proposed method for estimating the PDI can be readily adapted to deal with this setting.…”

Section: Discussionmentioning

confidence: 99%

The polytomous discrimination index for prediction involving multistate processes under intermittent observation

Jiang

Cook

2022

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

With the increasing importance of predictive modeling in health research comes the need for methods to rigorously assess predictive accuracy. We consider the problem of evaluating the accuracy of predictive models for nominal outcomes when outcome data are coarsened at random. We first consider the problem in the context of a multinomial response modeled by polytomous logistic regression. Attention is then directed to the motivating setting in which class membership corresponds to the state occupied in a multistate disease process at a time horizon of interest. Here, class (state) membership may be unknown at the time horizon since disease processes are under intermittent observation. We propose a novel extension to the polytomous discrimination index to address this and evaluate the predictive accuracy of an intensity-based model in the context of a study involving patients with arthritis from a registry at the University of Toronto Centre for Prognosis Studies in Rheumatic Diseases.

show abstract

“…Collection of large number of clinical and genomic data for individuals are initiated with the hope of finding clinically significant diagnostic and prognostic factors for diseases. In studies involving progression in joint damage in psoriatic arthritis, for example, interests may lie in detecting HLA alleles associated with different disease courses [1]. Along with the genomic data in clinical cohort studies, it is common to have time-to-event end points resulting in right-censored data [2].…”

Section: Introductionmentioning

confidence: 99%