Efficient Estimation for Semiparametric Structural Equation Models With Censored Data

Wong, Kin Yau; Zeng, Donglin; Lin, Danyu

doi:10.1080/01621459.2017.1299626

Cited by 8 publications

(7 citation statements)

References 33 publications

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“…The latent variables of FA are the covariates in the model for the event of interest and the censoring event. Related models for survival analysis have been previously considered for noninformative censoring [9][10][11][12][13][14][15] and informative censoring, 16 in the low-dimensional regime. The prior body of work on latent variable survival models has not considered the high-dimensional setting.…”

Section: Discussionmentioning

confidence: 99%

“…Most of the prior work has a univariate continuous or categorical latent space and none of the prior latent survival analysis work has commented on the value of the low‐dimensional projection of the data for visualizing and identifying heterogeneity in the individuals. Prior works on latent variable survival models with noninformative censoring include those by other authors and with informative censoring include that by Rowley et al Latent factors from FA and other latent variable models have been used for comparative Kaplan‐Meier analyses . High‐dimensional survival analysis has focused primarily on penalized methods to reduce parameters, and our method provides a complimentary approach.…”

Section: Introductionmentioning

confidence: 99%

“…Asparouhov and Masyn (2006) consider a continuous-time, mixed categorical and univariate continuous latent space model with fixed, binary, categorical, and continuous covariates, as well as noninformative censoring. Recently, Wong et al (2017) extended structural equation modeling, a continuous latent variable framework with fixed and parametrically distributed covariates, to include non-informative censoring.…”

Section: Introductionmentioning

confidence: 99%

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Factor analysis for survival time prediction with informative censoring and diverse covariates

McCurdy

Pachter

2019

Statistics in Medicine

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Fulfilling the promise of precision medicine requires accurately and precisely classifying disease states. For cancer, this includes prediction of survival time from a surfeit of covariates. Such data presents an opportunity for improved prediction, but also a challenge due to high dimensionality. Furthermore, disease populations can be heterogeneous. Integrative modeling is sensible, as the underlying hypothesis is that joint analysis of multiple covariates provides greater explanatory power than separate analyses. We propose an integrative latent variable model that combines factor analysis for various data types and an exponential proportional hazards (EPH) model for continuous survival time with informative censoring. The factor and EPH models are connected through low-dimensional latent variables that can be interpreted and visualized to identify subpopulations. We use this model to predict survival time. We demonstrate this model's utility in simulation and on four Cancer Genome Atlas datasets: diffuse lower-grade glioma, glioblastoma multiforme, lung adenocarcinoma, and lung squamous cell carcinoma. These datasets have small sample sizes, high-dimensional diverse covariates, and high censorship rates. We compare the predictions from our model to three alternative models. Our model outperforms in simulation and is competitive on real datasets. Furthermore, the low-dimensional visualization for diffuse lower-grade glioma displays known subpopulations. KEYWORDSdiffuse lower-grade glioma, exponential proportional hazards, factor analysis, glioblastoma multiforme, informative censoring, integrative models, latent variables, lung adenocarcinoma, lung squamous cell carcinoma Statistics in Medicine. 2019;38:3719-3732. wileyonlinelibrary.com/journal/sim

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Factor analysis for survival time prediction with informative censoring and diverse covariates

McCurdy

Pachter

2019

Statistics in Medicine

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“…For example, Stoolmiller (2016) used MSA to examine the interplay between short-term parentchild dynamics and the longer term development of externalizing problems (Stoolmiller, 2016). Recently MSA has been embedded within broader structural equation models (McCurdy, Molinaro, & Pachter, 2017;Stoolmiller & Snyder, 2014;Wong, Zeng, & Lin, 2018). This extension allows the temporal associations between behaviors-hazards-to be used as both predictors and outcomes in longitudinal studies.…”

Section: Advantages Of Survival Analysismentioning

confidence: 99%

Multilevel survival analysis: Studying the timing of children’s recurring behaviors.

Lougheed¹,

Benson²,

Cole³

et al. 2019

Developmental Psychology

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The timing of events (e.g., how long it takes a child to exhibit a particular behavior) is often of interest in developmental science. Multilevel survival analysis (MSA) is useful for examining behavioral timing in observational studies (i.e., video recordings) of children’s behavior. We illustrate how MSA can be used to answer two types of research questions. Specifically, using data from a study of 117 36-month-old (SD = .38) children during a frustration task, we examine the timing of 36-month-olds recurring anger expressions, and how it is related to: (1) negative affectivity, a dimension of temperament related to the ability to regulate emotions; and (2) children’s strategy use (distraction, bids to their mother). Contrary to expectations, negative affectivity was not associated with the timing of children’s recurring anger expressions. As expected, children’s recurring anger expressions were less likely to occur in the seconds when children were using a distraction strategy, whereas they were more likely when children made bids to their mother. MSA is a flexible analytic technique that, when applied to observational data, can yield valuable insights into the dynamics of children’s behaviors.

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“…23 The estimators are strongly consistent and asymptotically Gaussian, whereas the estimators of the Euclidean parameters achieve the semiparametric efficiency bound. Because the derivations of the theoretical properties of the estimators are largely similar to existing works, [24][25][26] we omit them here. There is an interesting contrast with the fixed change-point model.…”

Section: Introductionmentioning

confidence: 99%

Survival analysis with a random change-point

Lee

Wong

2023

Stat Methods Med Res

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Contemporary works in change-point survival models mainly focus on an unknown universal change-point shared by the whole study population. However, in some situations, the change-point is plausibly individual-specific, such as when it corresponds to the telomere length or menopausal age. Also, maximum-likelihood-based inference for the fixed change-point parameter is notoriously complicated. The asymptotic distribution of the maximum-likelihood estimator is non-standard, and computationally intensive bootstrap techniques are commonly used to retrieve its sampling distribution. This article is motivated by a breast cancer study, where the disease-free survival time of the patients is postulated to be regulated by the menopausal age, which is unobserved. As menopausal age varies across patients, a fixed change-point survival model may be inadequate. Therefore, we propose a novel proportional hazards model with a random change-point. We develop a nonparametric maximum-likelihood estimation approach and devise a stable expectation–maximization algorithm to compute the estimators. Because the model is regular, we employ conventional likelihood theory for inference based on the asymptotic normality of the Euclidean parameter estimators, and the variance of the asymptotic distribution can be consistently estimated by a profile-likelihood approach. A simulation study demonstrates the satisfactory finite-sample performance of the proposed methods, which yield small bias and proper coverage probabilities. The methods are applied to the motivating breast cancer study.

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Efficient Estimation for Semiparametric Structural Equation Models With Censored Data

Cited by 8 publications

References 33 publications

Factor analysis for survival time prediction with informative censoring and diverse covariates

Factor analysis for survival time prediction with informative censoring and diverse covariates

Multilevel survival analysis: Studying the timing of children’s recurring behaviors.

Survival analysis with a random change-point

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