<b>BeSS</b>: An <i>R</i> Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models

Wen, Canhong; Zhang, Aijun; Quan, Shijie; Wang, Xueqin

doi:10.18637/jss.v094.i04

Cited by 31 publications

(31 citation statements)

References 12 publications

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“…To determine the relevant predictor variables and avoid overfitting in the model, we chose the best model with the fewest variables while maintaining the prediction accuracy by using the best subset selection method with the Akaike information criterion (AIC) Akaike 19 . The final multivariable logistic regression model was established by the AIC best subset selection approach 20 . We report adjusted ORs (aORs) with 95% CIs for each predictor variable.…”

Section: Methodsmentioning

confidence: 99%

Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study

Guo

Tan

et al. 2021

Sci Rep

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Treating patients with COVID-19 is expensive, thus it is essential to identify factors on admission associated with hospital length of stay (LOS) and provide a risk assessment for clinical treatment. To address this, we conduct a retrospective study, which involved patients with laboratory-confirmed COVID-19 infection in Hefei, China and being discharged between January 20 2020 and March 16 2020. Demographic information, clinical treatment, and laboratory data for the participants were extracted from medical records. A prolonged LOS was defined as equal to or greater than the median length of hospitable stay. The median LOS for the 75 patients was 17 days (IQR 13–22). We used univariable and multivariable logistic regressions to explore the risk factors associated with a prolonged hospital LOS. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated. The median age of the 75 patients was 47 years. Approximately 75% of the patients had mild or general disease. The univariate logistic regression model showed that female sex and having a fever on admission were significantly associated with longer duration of hospitalization. The multivariate logistic regression model enhances these associations. Odds of a prolonged LOS were associated with male sex (aOR 0.19, 95% CI 0.05–0.63, p = 0.01), having fever on admission (aOR 8.27, 95% CI 1.47–72.16, p = 0.028) and pre-existing chronic kidney or liver disease (aOR 13.73 95% CI 1.95–145.4, p = 0.015) as well as each 1-unit increase in creatinine level (aOR 0.94, 95% CI 0.9–0.98, p = 0.007). We also found that a prolonged LOS was associated with increased creatinine levels in patients with chronic kidney or liver disease (p < 0.001). In conclusion, female sex, fever, chronic kidney or liver disease before admission and increasing creatinine levels were associated with prolonged LOS in patients with COVID-19.

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Section: Methodsmentioning

confidence: 99%

Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study

Guo

Tan

et al. 2021

Sci Rep

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“…All the analysis was performed using R software [ 20 ], particularly, the “MASS” [ 25 ], “BeSS” [ 26 ], and “glmnet” [ 27 ] packages for the forward and backward stepwise variable selection, best subset selection, and lasso regression, respectively. Furthermore “car” [ 28 ] was used for multicollinearity testing; “survival” [ 29 ] for the Kaplan-Meier curves, Cox model, extended Cox model, and proportional hazards testing; “survminer” [ 30 ] for the plotting of survival curves; “nlme” [ 31 ] for fitting the linear mixed-effects models; and “JM” [ 18 ] and “Jmbayes” [ 19 ] for fitting the joint models.…”

Section: Methodsmentioning

confidence: 99%

Predicting Biologic Therapy Outcome of Patients With Spondyloarthritis: Joint Models for Longitudinal and Survival Analysis

et al. 2021

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Background Rheumatic diseases are one of the most common chronic diseases worldwide. Among them, spondyloarthritis (SpA) is a group of highly debilitating diseases, with an early onset age, which significantly impacts patients’ quality of life, health care systems, and society in general. Recent treatment options consist of using biologic therapies, and establishing the most beneficial option according to the patients’ characteristics is a challenge that needs to be overcome. Meanwhile, the emerging availability of electronic medical records has made necessary the development of methods that can extract insightful information while handling all the challenges of dealing with complex, real-world data. Objective The aim of this study was to achieve a better understanding of SpA patients’ therapy responses and identify the predictors that affect them, thereby enabling the prognosis of therapy success or failure. Methods A data mining approach based on joint models for the survival analysis of the biologic therapy failure is proposed, which considers the information of both baseline and time-varying variables extracted from the electronic medical records of SpA patients from the database, Reuma.pt. Results Our results show that being a male, starting biologic therapy at an older age, having a larger time interval between disease start and initiation of the first biologic drug, and being human leukocyte antigen (HLA)–B27 positive are indicators of a good prognosis for the biological drug survival; meanwhile, having disease onset or biologic therapy initiation occur in more recent years, a larger number of education years, and higher values of C-reactive protein or Bath Ankylosing Spondylitis Functional Index (BASFI) at baseline are all predictors of a greater risk of failure of the first biologic therapy. Conclusions Among this Portuguese subpopulation of SpA patients, those who were male, HLA-B27 positive, and with a later biologic therapy starting date or a larger time interval between disease start and initiation of the first biologic therapy showed longer therapy adherence. Joint models proved to be a valuable tool for the analysis of electronic medical records in the field of rheumatic diseases and may allow for the identification of potential predictors of biologic therapy failure.

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“…Signature. We used "BeSS" R package utilizing the primaldual active set-based approach to select variables for the multicell type model [42]. Significantly, to minimize the risk of model overfitting, the maximum number of predictors was limited to 3 variables.…”

Section: Creation and Validation Of Prognostic Immune Cellmentioning

confidence: 99%

Bioinformatic Analysis Reveals Central Role for Tumor-Infiltrating Immune Cells in Uveal Melanoma Progression

Lachota

Lennikov

Malmberg

et al. 2021

Journal of Immunology Research

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Tumor-infiltrating immune cells are capable of effective cancer surveillance, and their abundance is linked to better prognosis in numerous tumor types. However, in uveal melanoma (UM), extensive immune infiltrate is associated with poor survival. This study aims to decipher the role of different tumor-infiltrating cell subsets in UM in order to identify potential targets for future immunotherapeutic treatment. We have chosen the TCGA-UVM cohort as a training dataset and GSE22138 as a testing dataset by mining publicly available databases. The abundance of 22 immune cell types was estimated using CIBERSORTx. Then, to determine the significance of tumor-infiltrating cell subsets in UM, we built a multicell type prognostic signature, which was validated in the testing cohort. The created signature was built upon the negative prognostic role of CD8+ T cells and M0 macrophages and the positive role of neutrophils. Based on the created signature score, we divided the patients into low- and high-risk groups. Kaplan-Meier, Cox, and ROC analyses demonstrated superior performance of our risk score compared to either clinical or pathologic characteristics of both cohorts. Further, we found the molecular pathways associated with cancer immunoevasion and metastasis to be enriched in the high-risk group, explaining both the lack of adequate immune surveillance despite increased infiltration of CD8+ T cells as well as the higher metastatic potential. Genes associated with tryptophan metabolism (IDO1 and KYNU) and metalloproteinases were among the most differentially expressed between the high- and low-risk groups. Our correlation analyses interpreted in context of published in vitro data strongly suggest the central role of CD8+ T cells in shifting the UM tumor microenvironment towards suppressive and metastasis-promoting. Therefore, we propose further investigations of IDO1 and metalloproteinases as novel targets for immunotherapy in lymphocyte-rich metastatic UM patients.

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BeSS: An R Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models

Cited by 31 publications

References 12 publications

Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study

Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study

Predicting Biologic Therapy Outcome of Patients With Spondyloarthritis: Joint Models for Longitudinal and Survival Analysis

Bioinformatic Analysis Reveals Central Role for Tumor-Infiltrating Immune Cells in Uveal Melanoma Progression

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