Semiparametric regression analysis for time-to-event marked endpoints in cancer studies

Hu, Chen; Tsodikov, Alex

doi:10.1093/biostatistics/kxt056

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…Second, our results were based on a cross-sectional study; it might to be thought as another limitation. In fact, as the basis of many other studies containing case-control study, cohort study and experimental study, a cross-sectional study has been proven to be of great value in many applications in fields as diverse as astronomy, biology, medicine, economics, and finance [43,44]. A well-designed cross-sectional study is enough to disclose the associations between variables though it will not lead to a clear cause-and-effect relationship among variables.…”

Section: Strength and Limitationsmentioning

confidence: 99%

Individual and joint association of bioavailable testosterone and aging with neutrophil-to-lymphocyte ratio in Chinese middle-aged and elderly men

et al. 2019

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Background and objectives Accumulating evidences suggest that chronic systemic inflammation (CSI) is independently associated with large number of major non-communicable chronic diseases (NCDs) ranging from metabolic disorders to cancers, and neutrophil-to-lymphocyte ratio (NLR) has been accepted as a novel, convenient marker for CSI response. Testosterone deficiency in men is linked to high risk of NCDs. This cross-sectional study aimed to investigate the individual and joint association of bioavailable testosterone (BIOT) and aging with NLR. Methods A total of 132 male adults were enrolled during Jan. 2011 and Oct. 2017 in the first affiliated hospital of University of Science and Technology of China. Local weighted regression (LOESS) and multivariable generalized linear regression models were utilized to comprehensively examine the individual and joint association between BIOT and age with NLR. Results Obvious linear relationships between NLR and BIOT or age were observed with the LOESS models. NLR was negatively correlated to BIOT after adjusting for some potential confounding factors (P = 0.034). As compared to the lowest quartile of BIOT, the adjusted decrease of NLR for the 2nd, 3rd and 4th quartiles were 0.40, 0.64 and 0.72, respectively. Meanwhile, NLR was observed to be independently correlated to elevated age (P = 0.043). Furthermore, as compared to the counterparts, men over 70 years combined with plasma BIOT less than 4.7 nmol/L had the highest NLR level, which suggested that low BIOT and aging jointly correlated to the level of NLR (P = 0.005). Conclusion BIOT deficiency and aging were individually and jointly correlated to CSI. Men over 70 years combined with BIOT < 4.7 nmol/L were more like to have higher grade of CSI than others.

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Section: Strength and Limitationsmentioning

confidence: 99%

Individual and joint association of bioavailable testosterone and aging with neutrophil-to-lymphocyte ratio in Chinese middle-aged and elderly men

et al. 2019

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“…For

η > μ

, the terminal event will be accelerated following the latent event (relative to such a Poisson process), while for

η < μ

the reverse is true. The baseline hazard

H false(\cdot false)

models the temporal pattern of the disease progression (see Hu and Tsodikov, , for a mechanistic justification and detailed discussion). Covariates

boldz

will enter the model through

μ

and

η

: specifically, for

β = {((), β_{0}, {boldβ}_{η}^{'}, {boldβ}_{μ}^{'})}^{'}

, we have

η = η false(β false) = e^{β_{0} + {boldz}^{'} {boldβ}_{η}}

and

μ = μ false(β false) = e^{{boldz}^{'} {boldβ}_{μ}}

.…”

Section: Model and Likelihoodmentioning

confidence: 99%

“…For η > μ, the terminal event will be accelerated following the latent event (relative to such a Poisson process), while for η < μ the reverse is true. The baseline hazard H(·) models the temporal pattern of the disease progression (see Hu and Tsodikov, 2014b, for a mechanistic justification and detailed discussion).…”

Section: Modelmentioning

confidence: 99%

“…While there has been some work done on such models (e.g., Gjessing, Aalen, and Hjort, 2003), the overwhelming body of research considers only frailties that are properly random variables, that is, are fixed at time 0. Hu and Tsodikov (2014b) develop a similar model for cancer progression that also makes use of a jump process as the frailty. However, they did not devise an efficient EM approach, which is the key contribution of the present article.…”

Section: Introductionmentioning

confidence: 99%

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Semiparametric Time-to-Event Modeling in the Presence of a Latent Progression Event

Rice

Tsodikov

2016

Biometrics

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Summary In cancer research, interest frequently centers on factors influencing a latent event that must precede a terminal event. In practice it is often impossible to observe the latent event precisely, making inference about this process difficult. To address this problem, we propose a joint model for the unobserved time to the latent and terminal events, with the two events linked by the baseline hazard. Covariates enter the model parametrically as linear combinations that multiply, respectively, the hazard for the latent event and the hazard for the terminal event conditional on the latent one. We derive the partial likelihood estimators for this problem assuming the latent event is observed, and propose a profile likelihood–based method for estimation when the latent event is unobserved. The baseline hazard in this case is estimated nonparametrically using the EM algorithm, which allows for closed-form Breslow-type estimators at each iteration, bringing improved computational efficiency and stability compared with maximizing the marginal likelihood directly. We present simulation studies to illustrate the finite-sample properties of the method; its use in practice is demonstrated in the analysis of a prostate cancer data set.

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“…To address the issue of latent stage, Hu and Tsodikov (2014b) proposed a semi-parametric regression model that is based on Markov modulated processes with non-parametric time transformation models in the setting of metastasis - recurrence paradigm where they model time-to-recurrence as a marked- end-point. We extended Hu and Tsodikov’s work to model the cancer process with more than two stages that can be latent or observed, terminal and non-terminal.…”

Section: Introductionmentioning

confidence: 99%

A joint model of cancer incidence, metastasis, and mortality

2017

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Many diseases, especially cancer, are not static, but rather can be summarized by a series of events or stages (e.g. diagnosis, remission, recurrence, metastasis, death). Most available methods to analyze multi-stage data ignore intermediate events and focus on the terminal event or consider (time to) multiple events as independent. Competing-risk or semi-competing-risk models are often deficient in describing the complex relationship between disease progression events which are driven by a shared progression stochastic process. A multi-stage model can only examine two stages at a time and thus fails to capture the effect of one stage on the time spent between other stages. Moreover, most models do not account for latent stages. We propose a semi-parametric joint model of diagnosis, latent metastasis, and cancer death and use nonparametric maximum likelihood to estimate covariate effects on the risks of intermediate events and death and the dependence between them. We illustrate the model with Monte Carlo simulations and analysis of real data on prostate cancer from the SEER database.

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Semiparametric regression analysis for time-to-event marked endpoints in cancer studies

Cited by 6 publications

References 19 publications

Individual and joint association of bioavailable testosterone and aging with neutrophil-to-lymphocyte ratio in Chinese middle-aged and elderly men

Individual and joint association of bioavailable testosterone and aging with neutrophil-to-lymphocyte ratio in Chinese middle-aged and elderly men

Semiparametric Time-to-Event Modeling in the Presence of a Latent Progression Event

A joint model of cancer incidence, metastasis, and mortality

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