A Power Prior Approach for Leveraging External Longitudinal and Competing Risks Survival Data Within the Joint Modeling Framework

2021 ◽  
Author(s):  
Md. Tuhin Sheikh ◽  
Ming-Hui Chen ◽  
Jonathan A. Gelfond ◽  
Joseph G. Ibrahim
Keyword(s):  
Competing Risks ◽  
Survival Data ◽  
Joint Modeling ◽  
Modeling Framework ◽  
Power Prior

Bayesian joint modeling of ordinal longitudinal measurements and competing risks survival data for analysing Tehran Lipid and Glucose Study

2020 ◽  
Vol 30 (4) ◽  
pp. 689-703
Author(s):  
Taban Baghfalaki ◽  
Shiva Kalantari ◽  
Mojtaba Ganjali ◽  
Farzad Hadaegh ◽  
Bagher Pahlavanzadeh
Keyword(s):  
Competing Risks ◽  
Survival Data ◽  
Joint Modeling ◽  
Longitudinal Measurements

scholarly journals Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer’s disease

2017 ◽  
Vol 28 (2) ◽  
pp. 327-342 ◽  
Author(s):  
Kan Li ◽  
Sheng Luo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Survival Data ◽  
Functional Data ◽  
Joint Modeling ◽  
Joint Model ◽  
Repeated Measurements ◽  
Joint Models ◽  
Modeling Framework ◽  
Dynamic Prediction

In the study of Alzheimer’s disease, researchers often collect repeated measurements of clinical variables, event history, and functional data. If the health measurements deteriorate rapidly, patients may reach a level of cognitive impairment and are diagnosed as having dementia. An accurate prediction of the time to dementia based on the information collected is helpful for physicians to monitor patients’ disease progression and to make early informed medical decisions. In this article, we first propose a functional joint model to account for functional predictors in both longitudinal and survival submodels in the joint modeling framework. We then develop a Bayesian approach for parameter estimation and a dynamic prediction framework for predicting the subjects’ future outcome trajectories and risk of dementia, based on their scalar and functional measurements. The proposed Bayesian functional joint model provides a flexible framework to incorporate many features both in joint modeling of longitudinal and survival data and in functional data analysis. Our proposed model is evaluated by a simulation study and is applied to the motivating Alzheimer’s Disease Neuroimaging Initiative study.

A Practical Guide to Family Studies with Lifetime Data

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Thomas H. Scheike ◽  
Klaus Kähler Holst
Keyword(s):  
Competing Risks ◽  
Survival Data ◽  
Age Of Onset ◽  
Familial Aggregation ◽  
Threshold Model ◽  
Right Censoring ◽  
Annual Review ◽  
Publication Date ◽  
Lifetime Data ◽  
Competing Risks Data

Familial aggregation refers to the fact that a particular disease may be overrepresented in some families due to genetic or environmental factors. When studying such phenomena, it is clear that one important aspect is the age of onset of the disease in question, and in addition, the data will typically be right-censored. Therefore, one must apply lifetime data methods to quantify such dependence and to separate it into different sources using polygenic modeling. Another important point is that the occurrence of a particular disease can be prevented by death—that is, competing risks—and therefore, the familial aggregation should be studied in a model that allows for both death and the occurrence of the disease. We here demonstrate how polygenic modeling can be done for both survival data and competing risks data dealing with right-censoring. The competing risks modeling that we focus on is closely related to the liability threshold model. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model

2018 ◽  
Vol 28 (10-11) ◽  
pp. 3392-3403 ◽  
Author(s):  
Jue Wang ◽  
Sheng Luo
Keyword(s):  
Survival Data ◽  
Repeated Measures ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Proportional Hazards Model ◽  
Latent Trait ◽  
Joint Modeling ◽  
Fine Motor ◽  
Terminal Event ◽  
Longitudinal Outcomes

Impairment caused by Amyotrophic lateral sclerosis (ALS) is multidimensional (e.g. bulbar, fine motor, gross motor) and progressive. Its multidimensional nature precludes a single outcome to measure disease progression. Clinical trials of ALS use multiple longitudinal outcomes to assess the treatment effects on overall improvement. A terminal event such as death or dropout can stop the follow-up process. Moreover, the time to the terminal event may be dependent on the multivariate longitudinal measurements. In this article, we develop a joint model consisting of a multidimensional latent trait linear mixed model (MLTLMM) for the multiple longitudinal outcomes, and a proportional hazards model with piecewise constant baseline hazard for the event time data. Shared random effects are used to link together two models. The model inference is conducted using a Bayesian framework via Markov chain Monte Carlo simulation implemented in Stan language. Our proposed model is evaluated by simulation studies and is applied to the Ceftriaxone study, a motivating clinical trial assessing the effect of ceftriaxone on ALS patients.

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

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.

scholarly journals Improved inference and prediction of bacterial genotype-phenotype associations using pangenome-spanning regressions

2019 ◽  
Author(s):  
John A. Lees ◽  
T. Tien Mai ◽  
Marco Galardini ◽  
Nicole E. Wheeler ◽  
Jukka Corander
Keyword(s):  
Genetic Variants ◽  
Statistical Power ◽  
Genome Wide Association Study ◽  
Bacterial Species ◽  
False Positive Rate ◽  
Model Fitting ◽  
Joint Modeling ◽  
Inference Procedure ◽  
Modeling Framework ◽  
Narrow Sense Heritability

ABSTRACTDiscovery of influential genetic variants and prediction of phenotypes such as antibiotic resistance are becoming routine tasks in bacterial genomics. Genome-wide association study (GWAS) methods can be applied to study bacterial populations, with a particular emphasis on alignment-free approaches, which are necessitated by the more plastic nature of bacterial genomes. Here we advance bacterial GWAS by introducing a computationally scalable joint modeling framework, where genetic variants covering the entire pangenome are compactly represented by unitigs, and the model fitting is achieved using elastic net penalization. In contrast to current leading GWAS approaches, which test each genotype-phenotype association separately for each variant, our joint modelling approach is shown to lead to increased statistical power while maintaining control of the false positive rate. Our inference procedure also delivers an estimate of the narrow-sense heritability, which is gaining considerable interest in studies of bacteria. Using an extensive set of state-of-the-art bacterial population genomic datasets we demonstrate that our approach performs accurate phenotype prediction, comparable to popular machine learning methods, while retaining both interpretability and computational efficiency. We expect that these advances will pave the way for the next generation of high-powered association and prediction studies for an increasing number of bacterial species.

scholarly journals The Effect of MSM and CD4+ Count on the Development of Cancer AIDS (AIDS-defining Cancer) and Non-cancer AIDS in the HAART Era

2019 ◽  
Vol 16 (4) ◽  
pp. 288-296
Author(s):  
Prosanta Mondal ◽  
Hyun J. Lim ◽  
OHTN Cohort Study Team
Keyword(s):  
Competing Risks ◽  
Joint Modeling ◽  
Study Data ◽  
Hiv Treatment ◽  
Cd4 Count ◽  
Survival Outcomes ◽  
Risk Category ◽  
Joint Models ◽  
Kaplan Meier ◽  
Sex With Men

Background: The HIV epidemic is increasing among Men who have Sex with Men (MSM) and the risk for AIDS defining cancer (ADC) is higher among them. Objective: To examine the effect of MSM and CD4+ count on time to cancer AIDS (ADC) and noncancer AIDS in competing risks setting in the HAART era. Method: Using Ontario HIV Treatment Network Cohort Study data, HIV-positive adults diagnosed between January 1997 and October 2012 having baseline CD4+ counts ≤ 500 cells/mm3 were evaluated. Two survival outcomes, cancer AIDS and non-cancer AIDS, were treated as competing risks. Kaplan-Meier analysis, Cox cause-specific hazards (CSH) model and joint modeling of longitudinal and survival outcomes were used. Results: Among the 822 participants, 657 (79.9%) were males; 686 (83.5%) received anti-retroviral (ARV) ever. Regarding risk category, the majority (58.5%) were men who have Sex with men (MSM). Mean age was 37.4 years (SD = 10.3). In the multivariate Cox CSH models, MSM were not associated with cancer AIDS but with non-cancer AIDS [HR = 2.92; P = 0.055, HR = 0.54; P = 0.0009, respectively]. However, in joint models of longitudinal and survival outcomes, MSM were associated with cancer AIDS but not with non-cancer AIDS [HR = 3.86; P = 0.013, HR = 0.73; P = 0.10]. CD4+ count, age, ARV ever were associated with both events in the joint models. Conclusion: This study demonstrates the importance of considering competing risks, and timedependent biomarker in the survival model. MSM have higher hazard for cancer AIDS. CD4+ count is associated with both survival outcomes.

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