Time-dependent tree-structured survival analysis with unbiased variable selection through permutation tests

Joint models are a powerful class of statistical models which apply to any data where event times are recorded alongside a longitudinal outcome by connecting longitudinal and time-to-event data within a joint likelihood allowing for quantification of the association between the two outcomes without possible bias. In order to make joint models feasible for regularization and variable selection, a statistical boosting algorithm has been proposed, which fits joint models using component-wise gradient boosting techniques. However, these methods have well-known limitations, i.e., they provide no balanced updating procedure for random effects in longitudinal analysis and tend to return biased effect estimation for time-dependent covariates in survival analysis. In this manuscript, we adapt likelihood-based boosting techniques to the framework of joint models and propose a novel algorithm in order to improve inference where gradient boosting has said limitations. The algorithm represents a novel boosting approach allowing for time-dependent covariates in survival analysis and in addition offers variable selection for joint models, which is evaluated via simulations and real world application modelling CD4 cell counts of patients infected with human immunodeficiency virus (HIV). Overall, the method stands out with respect to variable selection properties and represents an accessible way to boosting for time-dependent covariates in survival analysis, which lays a foundation for all kinds of possible extensions.

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Comparison of objective Bayes factors for variable selection in parametric regression models for survival analysis

Statistics in Medicine ◽

10.1002/sim.6249 ◽

2014 ◽

Vol 33 (26) ◽

pp. 4637-4654

Author(s):

Stefano Cabras ◽

Maria Eugenia Castellanos ◽

Silvia Perra

Keyword(s):

Survival Analysis ◽

Variable Selection ◽

Regression Models ◽

Bayes Factors ◽

Parametric Regression ◽

Objective Bayes ◽

Parametric Regression Models

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SURVIVAL MODELS IN THE CONTEXT OF MULTIPLICATIVE AND ADDITIVE HAZARDS

REVISTA BRASILEIRA DE BIOMETRIA ◽

10.28951/rbb.v37i3.391 ◽

2019 ◽

Vol 37 (3) ◽

pp. 306

Author(s):

Suely Ruiz GIOLO ◽

Jaqueline Aparecida RAMINELLI

Keyword(s):

Survival Analysis ◽

Primary Biliary Cirrhosis ◽

Goodness Of Fit ◽

Operating Characteristic ◽

Graphical Representation ◽

Predictive Accuracy ◽

Time Dependent ◽

Survival Models ◽

Biliary Cirrhosis ◽

Additive Hazards

In survival analysis, multiplicative and additive hazards models provide the two principal frameworks to study the association between the hazard and covariates. When these models are considered for analyzing a given survival dataset, it becomes relevant to evaluate the overall goodness-of-fit and how well each model can predict those subjects who subsequently will or will not experience the event. In this paper, this evaluation is based on a graphical representation of the Cox-Snell residuals and also on a time-dependent version of the area under the receiver operating characteristic (ROC) curve, denoted by AUC(t). A simulation study is carried out to evaluate the performance of the AUC(t) as a tool for comparing the predictive accuracy of survival models. A dataset from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver is also considered to illustrate the usefulness of these tools to compare survival models formulated under distinct hazards frameworks.

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Does Corporate Governance Affect the Financial Distress of Indonesian Company? A Survival Analysis Using Cox Hazard Model with Time-Dependent Covariates

Advanced Science Letters ◽

10.1166/asl.2016.8138 ◽

2016 ◽

Vol 22 (12) ◽

pp. 4326-4329 ◽

Cited By ~ 1

Author(s):

Farida Titik Kristanti ◽

Nury Effendi ◽

Aldrin Herwany ◽

Erie Febrian

Keyword(s):

Corporate Governance ◽

Survival Analysis ◽

Financial Distress ◽

Hazard Model ◽

Time Dependent ◽

Time Dependent Covariates ◽

Cox Hazard Model

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Survival analysis with time-dependent covariates subject to missing data or measurement error: Multiple Imputation for Joint Modeling (MIJM)

Biostatistics ◽

10.1093/biostatistics/kxx046 ◽

2017 ◽

Vol 19 (4) ◽

pp. 479-496 ◽

Cited By ~ 5

Author(s):

Margarita Moreno-Betancur ◽

John B Carlin ◽

Samuel L Brilleman ◽

Stephanie K Tanamas ◽

Anna Peeters ◽

...

Keyword(s):

Survival Analysis ◽

Missing Data ◽

Measurement Error ◽

Multiple Imputation ◽

Joint Modeling ◽

Time Dependent ◽

Time Dependent Covariates

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Construction of a novel mRNA-signature prediction model for prognosis of bladder cancer based on a statistical analysis

BMC Cancer ◽

10.1186/s12885-021-08611-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jianpeng Li ◽

Jinlong Cao ◽

Pan Li ◽

Zhiqiang Yao ◽

Ran Deng ◽

...

Keyword(s):

Bladder Cancer ◽

Survival Analysis ◽

Candidate Genes ◽

Risk Score ◽

Cox Regression ◽

Curve Analysis ◽

Functional Enrichment ◽

Time Dependent ◽

Stage Model ◽

Decision Curve Analysis

Abstract Background Bladder cancer (BC) is a common malignancy neoplasm diagnosed in advanced stages in most cases. It is crucial to screen ideal biomarkers and construct a more accurate prognostic model than conventional clinical parameters. The aim of this research was to develop and validate an mRNA-based signature for predicting the prognosis of patients with bladder cancer. Methods The RNA-seq data was downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were screened in three datasets, and prognostic genes were identified from the training set of TCGA dataset. The common genes between DEGs and prognostic genes were narrowed down to six genes via Least Absolute Shrinkage and Selection Operator (LASSO) regression, and stepwise multivariate Cox regression. Then the gene-based risk score was calculated via Cox coefficient. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess the prognostic power of risk score. Multivariate Cox regression analysis was applied to construct a nomogram. Decision curve analysis (DCA), calibration curves, and time-dependent ROC were performed to assess the nomogram. Finally, functional enrichment of candidate genes was conducted to explore the potential biological pathways of candidate genes. Results SORBS2, GPC2, SETBP1, FGF11, APOL1, and H1–2 were screened to be correlated with the prognosis of BC patients. A nomogram was constructed based on the risk score, pathological stage, and age. Then, the calibration plots for the 1-, 3-, 5-year OS were predicted well in entire TCGA-BLCA patients. Decision curve analysis (DCA) indicated that the clinical value of the nomogram was higher than the stage model and TNM model in predicting overall survival analysis. The time-dependent ROC curves indicated that the nomogram had higher predictive accuracy than the stage model and risk score model. The AUC of nomogram time-dependent ROC was 0.763, 0.805, and 0.806 for 1-year, 3-year, and 5-year, respectively. Functional enrichment analysis of candidate genes suggested several pathways and mechanisms related to cancer. Conclusions In this research, we developed an mRNA-based signature that incorporated clinical prognostic parameters to predict BC patient prognosis well, which may provide a novel prognosis assessment tool for clinical practice and explore several potential novel biomarkers related to the prognosis of patients with BC.

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