Survival stacking with multiple data types using pseudo-observation-based-AUC loss

Ginestet, Pablo Gonzalez; Gabriel, Erin E.; Sachs, Michael

doi:10.1080/10543406.2022.2041655

Cited by 3 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another technique that can be explored to optimize model performance is model stacking 43 . The predictions made by different base models are used as the input features of a meta model, generally a simple one, which generates the final prediction.…”

Section: Discussionmentioning

confidence: 99%

Comparison of models for stroke-free survival prediction in patients with CADASIL

Chhoa,

Chabriat,

Chevret

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, which is caused by mutations of the NOTCH3 gene, has a large heterogeneous progression, presenting with declines of various clinical scores and occurrences of various clinical event. To help assess disease progression, this work focused on predicting the composite endpoint of stroke-free survival time by comparing the performance of Cox proportional hazards regression to that of machine learning models using one of four feature selection approaches applied to demographic, clinical and magnetic resonance imaging observational data collected from a study cohort of 482 patients. The quality of the modeling process and the predictive performance were evaluated in a nested cross-validation procedure using the time-dependent Brier Score and AUC at 5 years from baseline, the former measuring the overall performance including calibration and the latter highlighting the discrimination ability, with both metrics taking into account the presence of right-censoring. The best model for each metric was the componentwise gradient boosting model with a mean Brier score of 0.165 and the random survival forest model with a mean AUC of 0.773, both combined with the LASSO feature selection method.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparison of models for stroke-free survival prediction in patients with CADASIL

Chhoa,

Chabriat,

Chevret

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The pseudo-value technique was first postulated by Andersen and his colleagues [ 18 , 19 ] in the context of multi-state survival models with right-censored data. Since then, it has been well studied in various disciplines of statistics including the interval-censored data [ 20 , 21 ], clustered data [ 22 , 23 ], and machine learning methods [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Differential network connectivity analysis for microbiome data adjusted for clinical covariates using jackknife pseudo-values

Ahn,

Datta

2024

BMC Bioinformatics

View full text Add to dashboard Cite

Background A recent breakthrough in differential network (DN) analysis of microbiome data has been realized with the advent of next-generation sequencing technologies. The DN analysis disentangles the microbial co-abundance among taxa by comparing the network properties between two or more graphs under different biological conditions. However, the existing methods to the DN analysis for microbiome data do not adjust for other clinical differences between subjects. Results We propose a Statistical Approach via Pseudo-value Information and Estimation for Differential Network Analysis (SOHPIE-DNA) that incorporates additional covariates such as continuous age and categorical BMI. SOHPIE-DNA is a regression technique adopting jackknife pseudo-values that can be implemented readily for the analysis. We demonstrate through simulations that SOHPIE-DNA consistently reaches higher recall and F1-score, while maintaining similar precision and accuracy to existing methods (NetCoMi and MDiNE). Lastly, we apply SOHPIE-DNA on two real datasets from the American Gut Project and the Diet Exchange Study to showcase the utility. The analysis of the Diet Exchange Study is to showcase that SOHPIE-DNA can also be used to incorporate the temporal change of connectivity of taxa with the inclusion of additional covariates. As a result, our method has found taxa that are related to the prevention of intestinal inflammation and severity of fatigue in advanced metastatic cancer patients. Conclusion SOHPIE-DNA is the first attempt of introducing the regression framework for the DN analysis in microbiome data. This enables the prediction of characteristics of a connectivity of a network with the presence of additional covariate information in the regression. The package with a vignette of our methodology is available through the CRAN repository (https://CRAN.R-project.org/package=SOHPIE), named SOHPIE (pronounced as Sofie). The source code and user manual can be found at https://github.com/sjahnn/SOHPIE-DNA.

show abstract

A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data

Shen,

Wang,

Sun

et al. 2024

BMC Med Res Methodol

View full text Add to dashboard Cite

Background Survival prediction using high-dimensional molecular data is a hot topic in the field of genomics and precision medicine, especially for cancer studies. Considering that carcinogenesis has a pathway-based pathogenesis, developing models using such group structures is a closer mimic of disease progression and prognosis. Many approaches can be used to integrate group information; however, most of them are single-model methods, which may account for unstable prediction. Methods We introduced a novel survival stacking method that modeled using group structure information to improve the robustness of cancer survival prediction in the context of high-dimensional omics data. With a super learner, survival stacking combines the prediction from multiple sub-models that are independently trained using the features in pre-grouped biological pathways. In addition to a non-negative linear combination of sub-models, we extended the super learner to non-negative Bayesian hierarchical generalized linear model and artificial neural network. We compared the proposed modeling strategy with the widely used survival penalized method Lasso Cox and several group penalized methods, e.g., group Lasso Cox, via simulation study and real-world data application. Results The proposed survival stacking method showed superior and robust performance in terms of discrimination compared with single-model methods in case of high-noise simulated data and real-world data. The non-negative Bayesian stacking method can identify important biological signal pathways and genes that are associated with the prognosis of cancer. Conclusions This study proposed a novel survival stacking strategy incorporating biological group information into the cancer prognosis models. Additionally, this study extended the super learner to non-negative Bayesian model and ANN, enriching the combination of sub-models. The proposed Bayesian stacking strategy exhibited favorable properties in the prediction and interpretation of complex survival data, which may aid in discovering cancer targets.

show abstract

Survival stacking with multiple data types using pseudo-observation-based-AUC loss

Cited by 3 publications

References 25 publications

Comparison of models for stroke-free survival prediction in patients with CADASIL

Comparison of models for stroke-free survival prediction in patients with CADASIL

Differential network connectivity analysis for microbiome data adjusted for clinical covariates using jackknife pseudo-values

A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data

Contact Info

Product

Resources

About