A new method for analysing transition to psychosis: Joint modelling of time-to-event outcome with time-dependent predictors

Abstract In end-use, containerboard is subjected to a variety of loading histories, such as seconds of loading/unloading, hours of vibration, days of creep load. The fundamental question is whether the commonly measured static strength represents “strength” under these conditions. Another question is, since those time-dependent failures are notoriously variable, how to describe the probabilistic aspect. This study concerns the characterisation of these different facets of “strength”. In our earlier work, we have investigated the theoretical framework for time-dependent, probabilistic failures, and identified three material parameters: (1) characteristic strength, {S_{c}}, representing short-term strength, (2) brittleness/durability parameter, ρ, and (3) reliability parameter, β. We have also developed a new method that allows us to determine all these parameters much faster than typical creep tests. Using the new method, we have started investigating effects of basic papermaking variables on the new material parameters. Among the samples tested, the parameter ρ varied from 20 to 50, and β from 0.5 to 1.0. This suggests that, even within the current papermaking practice, there is a wide operating window to tune these new material parameters. The future work is, therefore, to find specific manufacturing variables that can systematically change these new material parameters.

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A joint modeling approach for longitudinal microbiome data improves ability to detect microbiome associations with disease

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008473 ◽

2020 ◽

Vol 16 (12) ◽

pp. e1008473

Author(s):

Pamela N. Luna ◽

Jonathan M. Mansbach ◽

Chad A. Shaw

Keyword(s):

Joint Modeling ◽

Mixed Effects ◽

Mixed Effects Model ◽

Time Dependent ◽

Time To Event ◽

Modeling Framework ◽

Microbial Composition ◽

Disease Outcomes ◽

Microbiome Data ◽

Over Time

Changes in the composition of the microbiome over time are associated with myriad human illnesses. Unfortunately, the lack of analytic techniques has hindered researchers’ ability to quantify the association between longitudinal microbial composition and time-to-event outcomes. Prior methodological work developed the joint model for longitudinal and time-to-event data to incorporate time-dependent biomarker covariates into the hazard regression approach to disease outcomes. The original implementation of this joint modeling approach employed a linear mixed effects model to represent the time-dependent covariates. However, when the distribution of the time-dependent covariate is non-Gaussian, as is the case with microbial abundances, researchers require different statistical methodology. We present a joint modeling framework that uses a negative binomial mixed effects model to determine longitudinal taxon abundances. We incorporate these modeled microbial abundances into a hazard function with a parameterization that not only accounts for the proportional nature of microbiome data, but also generates biologically interpretable results. Herein we demonstrate the performance improvements of our approach over existing alternatives via simulation as well as a previously published longitudinal dataset studying the microbiome during pregnancy. The results demonstrate that our joint modeling framework for longitudinal microbiome count data provides a powerful methodology to uncover associations between changes in microbial abundances over time and the onset of disease. This method offers the potential to equip researchers with a deeper understanding of the associations between longitudinal microbial composition changes and disease outcomes. This new approach could potentially lead to new diagnostic biomarkers or inform clinical interventions to help prevent or treat disease.

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Joint modelling of longitudinal and time-to-event data: An illustration using CD4 count and mortality in a cohort of patients initiated on antiretroviral therapy

10.21203/rs.2.19995/v1 ◽

2020 ◽

Author(s):

Nobuhle Nokubonga Mchunu ◽

Henry Mwambi ◽

Tarylee Reddy ◽

Nonhlanhla Yende-Zuma ◽

Kogieleum Naidoo

Keyword(s):

Cox Model ◽

Joint Model ◽

Cd4 Count ◽

Time Varying ◽

Event Data ◽

Time To Event ◽

Joint Modelling ◽

Risk Of Death ◽

Time To Event Data ◽

Over Time

Abstract Background: Modelling of longitudinal biomarkers and time-to-event data are important to monitor disease progression. However, these two variables are traditionally analyzed separately or time-varying Cox models are used. The former strategy fails to recognize the shared random-effects from the two processes while the latter assumes that longitudinal biomarkers are exogenous covariates, resulting in inefficient or biased estimates for the time-to-event model. Therefore, we used joint modelling for longitudinal and time-to-event data to assess the effect of longitudinal CD4 count on mortality. Methods: We studied 4014 patients from the Centre for the AIDS Programme of Research in South Africa (CAPRISA) who initiated ART between June 2004 and August 2013. We used proportional hazards regression model to assess the effect of baseline characteristics (excluding CD4 count) on mortality, and linear mixed effect models to evaluate the effect of baseline characteristics on the CD4 count evolution over time. Thereafter, the two analytical approaches were amalgamated to form an advanced joint model for studying the effect of longitudinal CD4 count on mortality. To illustrate the virtues of the joint model, the results from the joint model were compared to those from the time-varying Cox model. Results: Using joint modelling, we found that lower CD4 count over time was associated with a 1.3-fold increase in the risk of death, (HR: 1.34, 95% CI: 1.27-1.42). Whereas, results from the time-varying Cox model showed lower CD4 count over time was associated with a 1.2-fold increase in the risk of death, (HR: 1.17, 95% CI: 1.12-1.23). Conclusions: Joint modelling enabled the assessment of the effect of longitudinal CD4 count on mortality while correcting for shared random effects between longitudinal and time-to-event models. In the era of universal test and treat, the evaluation of CD4 count is still crucial for guiding the initiation and discontinuation of opportunistic infections prophylaxis and assessment of late presenting patients. CD4 count can also be used when immunological failure is suspected as we have shown that it is associated with mortality. Keywords: Time-to-event data; longitudinal data; joint models; CD4 count; mortality; bias

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Revisiting Methods For Modeling Longitudinal and Survival Data: The Framingham Heart Study

10.21203/rs.3.rs-33952/v3 ◽

2021 ◽

Author(s):

Julius S Ngwa ◽

Howard J Cabral ◽

Debbie M Cheng ◽

David R Gagnon ◽

Michael P LaValley ◽

...

Keyword(s):

Maximum Likelihood ◽

Longitudinal Data ◽

Survival Data ◽

Framingham Heart Study ◽

Time Dependent ◽

Time To Event ◽

Longitudinal Measure ◽

Heart Study ◽

Longitudinal And Survival Data ◽

Joint Method

Abstract Background: Statistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome. Methods: In this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times. Results: Simulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years.Conclusions: Traditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

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