Incorporating biological networks into high-dimensional Bayesian survival analysis using an ICM/M algorithm

2021 ◽  
Author(s):  
Vitara Pungpapong
Keyword(s):  
Variable Selection ◽  
Proportional Hazards Model ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Cox Model ◽  
Cox Proportional Hazards Model ◽  
Adaptive Lasso ◽  
Genomic Research ◽  
High Dimensional ◽  
Cox Models

The Cox proportional hazards model has been widely used in cancer genomic research that aims to identify genes from high-dimensional gene expression space associated with the survival time of patients. With the increase in expertly curated biological pathways, it is challenging to incorporate such complex networks in fitting a high-dimensional Cox model. This paper considers a Bayesian framework that employs the Ising prior to capturing relations among genes represented by graphs. A spike-and-slab prior is also assigned to each of the coefficients for the purpose of variable selection. The iterated conditional modes/medians (ICM/M) algorithm is proposed for the implementation for Cox models. The ICM/M estimates hyperparameters using conditional modes and obtains coefficients through conditional medians. This procedure produces some coefficients that are exactly zero, making the model more interpretable. Comparisons of the ICM/M and other regularized Cox models were carried out with both simulated and real data. Compared to lasso, adaptive lasso, elastic net, and DegreeCox, the ICM/M yielded more parsimonious models with consistent variable selection. The ICM/M model also provided a smaller number of false positives than the other methods and showed promising results in terms of predictive accuracy. In terms of computing times among the network-aware methods, the ICM/M algorithm is substantially faster than DegreeCox even when incorporating a large complex network. The implementation of the ICM/M algorithm for Cox regression model is provided in R package icmm, available on the Comprehensive R Archive Network (CRAN).

Proportionally Difficult: Testing for Nonproportional Hazards in Cox Models

2010 ◽  
Vol 18 (2) ◽  
pp. 189-205 ◽  
Author(s):  
Luke Keele
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Standard Test ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Cox Models ◽  
Nonproportional Hazards ◽  
Specification Errors ◽  
Hazards Model

The Cox proportional hazards model is widely used to model durations in the social sciences. Although this model allows analysts to forgo choices about the form of the hazard, it demands careful attention to the proportional hazards assumption. To this end, a standard diagnostic method has been developed to test this assumption. I argue that the standard test for nonproportional hazards has been misunderstood in current practice. This test detects a variety of specification errors, and these specification errors must be corrected before one can correctly diagnose nonproportionality. In particular, unmodeled nonlinearity can appear as a violation of the proportional hazard assumption for the Cox model. Using both simulation and empirical examples, I demonstrate how an analyst might be led astray by incorrectly applying the nonproportionality test.

scholarly journals hdnom: Building Nomograms for Penalized Cox Models with High-Dimensional Survival Data

2016 ◽  
Author(s):  
Nan Xiao ◽  
Qing-Song Xu ◽  
Miao-Zhu Li
Keyword(s):  
Survival Data ◽  
Web Application ◽  
Model Building ◽  
Cox Regression ◽  
Cox Model ◽  
R Package ◽  
High Dimensional ◽  
Cox Models ◽  
Link Type ◽  
Open Source Software Package

AbstractSummaryWe developed hdnom, an R package for survival modeling with high-dimensional data. The package is the first free and open-source software package that streamlines the workflow of penalized Cox model building, validation, calibration, comparison, and nomogram visualization, with nine types of penalized Cox regression methods fully supported. A web application and an online prediction tool maker are offered to enhance interac-tivity and flexibility in high-dimensional survival analysis.AvailabilityThe hdnom R package is available from CRAN:https://cran.r-project.org/package=hdnomunder GPL. The hdnom web application can be accessed athttp://hdnom.io. The web application maker is available fromhttp://hdnom.org/appmaker. The hdnom project website:http://[email protected]@duke.edu

Adaptive lasso for the Cox regression with interval censored and possibly left truncated data

2019 ◽  
Vol 29 (4) ◽  
pp. 1243-1255 ◽  
Author(s):  
Chenxi Li ◽  
Daewoo Pak ◽  
David Todem
Keyword(s):  
Censored Data ◽  
Proportional Hazards Model ◽  
Cox Regression ◽  
Cox Proportional Hazards Model ◽  
Oracle Property ◽  
Adaptive Lasso ◽  
Interval Censored Data ◽  
Operating Characteristics ◽  
Data Set ◽  
Interval Censored

We propose a penalized variable selection method for the Cox proportional hazards model with interval censored data. It conducts a penalized nonparametric maximum likelihood estimation with an adaptive lasso penalty, which can be implemented through a penalized EM algorithm. The method is proven to enjoy the desirable oracle property. We also extend the method to left truncated and interval censored data. Our simulation studies show that the method possesses the oracle property in samples of modest sizes and outperforms available existing approaches in many of the operating characteristics. An application to a dental caries data set illustrates the method's utility.

Comparing Partial Likelihood and Robust Estimation Methods for the Cox Regression Model

2012 ◽  
Vol 20 (1) ◽  
pp. 113-135 ◽  
Author(s):  
Bruce A. Desmarais ◽  
Jeffrey J. Harden
Keyword(s):  
Proportional Hazards Model ◽  
Cox Regression ◽  
Cox Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Partial Likelihood ◽  
Estimation Methods ◽  
Omitted Variables ◽  
Science Data ◽  
Political Processes

The Cox proportional hazards model is ubiquitous in time-to-event studies of political processes. Plausible deviations from correct specification and operationalization caused by problems such as measurement error or omitted variables can produce substantial bias when the Cox model is estimated by conventional partial likelihood maximization (PLM). One alternative is an iteratively reweighted robust (IRR) estimator, which can reduce this bias. However, the utility of IRR is limited by the fact that there is currently no method for determining whether PLM or IRR is more appropriate for a particular sample of data. Here, we develop and evaluate a novel test for selecting between the two estimators. Then, we apply the test to political science data. We demonstrate that PLM and IRR can each be optimal, that our test is effective in choosing between them, and that substantive conclusions can depend on which one is used.

scholarly journals High-Dimensional Cox Regression Analysis in Genetic Studies with Censored Survival Outcomes

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Jinfeng Xu
Keyword(s):  
Relative Risk ◽  
Variable Selection ◽  
Cox Regression ◽  
Cox Model ◽  
Partial Likelihood ◽  
Likelihood Method ◽  
Dimensional Case ◽  
High Dimensional ◽  
Cox Regression Analysis ◽  
Penalization Methods

With the advancement of high-throughput technologies, nowadays high-dimensional genomic and proteomic data are easy to obtain and have become ever increasingly important in unveiling the complex etiology of many diseases. While relating a large number of factors to a survival outcome through the Cox relative risk model, various techniques have been proposed in the literature. We review some recently developed methods for such analysis. For high-dimensional variable selection in the Cox model with parametric relative risk, we consider the univariate shrinkage method (US) using the lasso penalty and the penalized partial likelihood method using the folded penalties (PPL). The penalization methods are not restricted to the finite-dimensional case. For the high-dimensional (p→∞,p≪n) or ultrahigh-dimensional case (n→∞,n≪p), both the sure independence screening (SIS) method and the extended Bayesian information criterion (EBIC) can be further incorporated into the penalization methods for variable selection. We also consider the penalization method for the Cox model with semiparametric relative risk, and the modified partial least squares method for the Cox model. The comparison of different methods is discussed and numerical examples are provided for the illustration. Finally, areas of further research are presented.

scholarly journals 1388. Dose Discrimination for ASN100: Bridging from Rabbit Survival Data to Predicted Activity in Humans Using a Minimal Physiologically Based Pharmacokinetic (mPBPK) Model

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S426-S426
Author(s):  
Christopher M Rubino ◽  
Lukas Stulik ◽  
Harald Rouha ◽  
Zehra Visram ◽  
Adriana Badarau ◽  
...  
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Virulent Strain ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Simulated Patients ◽  
Research Support ◽  
Mpbpk Model

Abstract Background ASN100 is a combination of two co-administered fully human monoclonal antibodies (mAbs), ASN-1 and ASN-2, that together neutralize the six cytotoxins critical to S. aureus pneumonia pathogenesis. ASN100 is in development for prevention of S. aureus pneumonia in mechanically ventilated patients. A pharmacometric approach to dose discrimination in humans was taken in order to bridge from dose-ranging, survival studies in rabbits to anticipated human exposures using a mPBPK model derived from data from rabbits (infected and noninfected) and noninfected humans [IDWeek 2017, Poster 1849]. Survival in rabbits was assumed to be indicative of a protective effect through ASN100 neutralization of S. aureus toxins. Methods Data from studies in rabbits (placebo through 20 mg/kg single doses of ASN100, four strains representing MRSA and MSSA isolates with different toxin profiles) were pooled with data from a PK and efficacy study in infected rabbits (placebo and 40 mg/kg ASN100) [IDWeek 2017, Poster 1844]. A Cox proportional hazards model was used to relate survival to both strain and mAb exposure. Monte Carlo simulation was then applied to generate ASN100 exposures for simulated patients given a range of ASN100 doses and infection with each strain (n = 500 per scenario) using a mPBPK model. Using the Cox model, the probability of full protection from toxins (i.e., predicted survival) was estimated for each simulated patient. Results Cox models showed that survival in rabbits is dependent on both strain and ASN100 exposure in lung epithelial lining fluid (ELF). At human doses simulated (360–10,000 mg of ASN100), full or substantial protection is expected for all four strains tested. For the most virulent strain tested in the rabbit pneumonia study (a PVL-negative MSSA, Figure 1), the clinical dose of 3,600 mg of ASN100 provides substantially higher predicted effect relative to lower doses, while doses above 3,600 mg are not predicted to provide significant additional protection. Conclusion A pharmacometric approach allowed for the translation of rabbit survival data to infected patients as well as discrimination of potential clinical doses. These results support the ASN100 dose of 3,600 mg currently being evaluated in a Phase 2 S. aureus pneumonia prevention trial. Disclosures C. M. Rubino, Arsanis, Inc.: Research Contractor, Research support. L. Stulik, Arsanis Biosciences GmbH: Employee, Salary. H. Rouha, 3Arsanis Biosciences GmbH: Employee, Salary. Z. Visram, Arsanis Biosciences GmbH: Employee, Salary. A. Badarau, Arsanis Biosciences GmbH: Employee, Salary. S. A. Van Wart, Arsanis, Inc.: Research Contractor, Research support. P. G. Ambrose, Arsanis, Inc.: Research Contractor, Research support. M. M. Goodwin, Arsanis, Inc.: Employee, Salary. E. Nagy, Arsanis Biosciences GmbH: Employee, Salary.

scholarly journals Incidence and Predictors of Hospitalization in Patients with Atrial Fibrillation: Results from the Chinese Atrial Fibrillation Registry Study

2020 ◽  
Author(s):  
Zhaojie Dong ◽  
Xin Du ◽  
Shangxin Lu ◽  
Chao Jiang ◽  
Shijun Xia ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Cardiovascular Diseases ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Chronic Obstructive ◽  
Unique Identifier ◽  
Clinical Trial Registration

Abstract Background: Patients with atrial fibrillation (AF) underwent a high risk of hospitalization, which, however, has not been paid much attention in clinic. Therefore, we aimed to assess the incidence, causes and predictors of hospitalization in AF patients.Methods: From August 2011 to December 2017, 20,172 AF patients from the Chinese Atrial Fibrillation Registry (China-AF) Study were enrolled in this study. We described the incidence, causes of hospitalization according to age and gender categories. The Cox proportional hazards model was employed to identify predictors of first all-cause and first cause-specific hospitalization. Results: After a mean follow-up of 37.3 ± 20.4 months, 7,512 (37.2%) AF patients experienced one or more hospitalizations. The overall incidence of all-cause hospitalization was 24.0 per 100 patient-years. Patients aged < 65 years were predominantly hospitalized for AF (42.1% of the total frequency of hospitalizations); while patients aged 65-74 and ≥ 75 years were mainly hospitalized for non-cardiovascular diseases (43.6% and 49.3%, respectively). Multivariate Cox model analysis verified the higher risk of hospitalization in patients complicated with heart failure (HF)[hazard ratio (HR) 1.15, 95% confidence interval (CI) 1.08-1.24], established coronary artery disease (CAD) (HR 1.26, 95%CI 1.19-1.34), ischemic stroke/transient ischemic attack (TIA) (HR 1.26, 95%CI 1.18-1.33), diabetes (HR 1.16, 95%CI 1.10-1.22), chronic obstructive pulmonary disease (COPD) (HR 1.41, 95%CI 1.13-1.76), gastrointestinal disorder (HR 1.39, 95%CI 1.23-1.58), and renal dysfunction (HR 1.31, 95%CI 1.16-1.48). Conclusions: More than one-third of AF patients included in this study were hospitalized at least once during almost 3 years of follow-up. The main cause for hospitalization among elderly patients (≥65 years) is non-cardiovascular diseases rather than AF. Multidisciplinary management of comorbidities should be advocated as strategies to reduce hospitalization in AF patients.Clinical Trial Registration: URL: http://www.chictr.org.cn/showproj.aspx?proj=5831. Unique identifier: ChiCTR-OCH-13003729.

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