BayesSPsurv: An R Package to Estimate Bayesian (Spatial) Split-Population Survival Models

Zhou and Hanson; Zhou and Hanson; Zhou and Hanson ( 2015 , Nonparametric Bayesian Inference in Biostatistics, pages 215–46. Cham: Springer; 2018, Journal of the American Statistical Association, 113, 571–81; 2020, spBayesSurv: Bayesian Modeling and Analysis of Spatially Correlated Survival Data. R package version 1.1.4) and Zhou et al. (2020, Journal of Statistical Software, Articles, 92, 1–33) present methods for estimating spatial survival models using areal data. This article applies their methods to a dataset recording New Orleans business decisions to re-open after Hurricane Katrina; the data were included in LeSage et al. (2011b , Journal of the Royal Statistical Society: Series A (Statistics in Society), 174, 1007—27). In two articles ( LeSage etal., 2011a , Significance, 8, 160—63; 2011b, Journal of the Royal Statistical Society: Series A (Statistics in Society), 174, 1007—27), spatial probit models are used to model spatial dependence in this dataset, with decisions to re-open aggregated to the first 90, 180 and 360 days. We re-cast the problem as one of examining the time-to-event records in the data, right-censored as observations ceased before 175 businesses had re-opened; we omit businesses already re-opened when observations began on Day 41. We are interested in checking whether the conclusions about the covariates using aspatial and spatial probit models are modified when applying survival and spatial survival models estimated using MCMC and INLA. In general, we find that the same covariates are associated with re-opening decisions in both modelling approaches. We do however find that data collected from three streets differ substantially, and that the streets are probably better handled separately or that the street effect should be included explicitly.

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Cost-effectiveness Analysis in R Using a Multi-state Modeling Survival Analysis Framework: A Tutorial

Medical Decision Making ◽

10.1177/0272989x16651869 ◽

2016 ◽

Vol 37 (4) ◽

pp. 340-352 ◽

Cited By ~ 19

Author(s):

Claire Williams ◽

James D. Lewsey ◽

Andrew H. Briggs ◽

Daniel F. Mackay

Keyword(s):

Cost Effectiveness ◽

Markov Property ◽

R Package ◽

Cost Effectiveness Analysis ◽

Sensitivity Analyses ◽

Survival Models ◽

Decision Analytic Model ◽

Modeling Approach ◽

Effectiveness Analysis ◽

Markov Decision

This tutorial provides a step-by-step guide to performing cost-effectiveness analysis using a multi-state modeling approach. Alongside the tutorial, we provide easy-to-use functions in the statistics package R. We argue that this multi-state modeling approach using a package such as R has advantages over approaches where models are built in a spreadsheet package. In particular, using a syntax-based approach means there is a written record of what was done and the calculations are transparent. Reproducing the analysis is straightforward as the syntax just needs to be run again. The approach can be thought of as an alternative way to build a Markov decision-analytic model, which also has the option to use a state-arrival extended approach. In the state-arrival extended multi-state model, a covariate that represents patients’ history is included, allowing the Markov property to be tested. We illustrate the building of multi-state survival models, making predictions from the models and assessing fits. We then proceed to perform a cost-effectiveness analysis, including deterministic and probabilistic sensitivity analyses. Finally, we show how to create 2 common methods of visualizing the results—namely, cost-effectiveness planes and cost-effectiveness acceptability curves. The analysis is implemented entirely within R. It is based on adaptions to functions in the existing R package mstate to accommodate parametric multi-state modeling that facilitates extrapolation of survival curves.

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A new R package for actuarial survival models

Computational Statistics ◽

10.1007/s00180-013-0400-2 ◽

2013 ◽

Vol 28 (5) ◽

pp. 2139-2160 ◽

Cited By ~ 3

Author(s):

S. Nadarajah ◽

S. A. A. Bakar

Keyword(s):

R Package ◽

Survival Models ◽

Actuarial Survival

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spatsurv: An R Package for Bayesian Inference with Spatial Survival Models

Journal of Statistical Software ◽

10.18637/jss.v077.i04 ◽

2017 ◽

Vol 77 (4) ◽

Cited By ~ 4

Author(s):

Benjamin M. Taylor ◽

Barry S. Rowlingson

Keyword(s):

Bayesian Inference ◽

R Package ◽

Survival Models

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COSMONET: An R Package for Survival Analysis Using Screening-Network Methods

Mathematics ◽

10.3390/math9243262 ◽

2021 ◽

Vol 9 (24) ◽

pp. 3262

Author(s):

Antonella Iuliano ◽

Annalisa Occhipinti ◽

Claudia Angelini ◽

Italia De De Feis ◽

Pietro Liò

Keyword(s):

Survival Analysis ◽

Cancer Survival ◽

Cox Regression ◽

Dimensional Space ◽

Prognostic Index ◽

Enrichment Analysis ◽

R Package ◽

Survival Models ◽

Survival Prediction ◽

Risk Class

Identifying relevant genomic features that can act as prognostic markers for building predictive survival models is one of the central themes in medical research, affecting the future of personalized medicine and omics technologies. However, the high dimension of genome-wide omic data, the strong correlation among the features, and the low sample size significantly increase the complexity of cancer survival analysis, demanding the development of specific statistical methods and software. Here, we present a novel R package, COSMONET (COx Survival Methods based On NETworks), that provides a complete workflow from the pre-processing of omics data to the selection of gene signatures and prediction of survival outcomes. In particular, COSMONET implements (i) three different screening approaches to reduce the initial dimension of the data from a high-dimensional space p to a moderate scale d, (ii) a network-penalized Cox regression algorithm to identify the gene signature, (iii) several approaches to determine an optimal cut-off on the prognostic index (PI) to separate high- and low-risk patients, and (iv) a prediction step for patients’ risk class based on the evaluation of PIs. Moreover, COSMONET provides functions for data pre-processing, visualization, survival prediction, and gene enrichment analysis. We illustrate COSMONET through a step-by-step R vignette using two cancer datasets.

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survxai: an R package for structure-agnostic explanations of survival models

The Journal of Open Source Software ◽

10.21105/joss.00961 ◽

2018 ◽

Vol 3 (31) ◽

pp. 961

Author(s):

Aleksandra Grudziaz ◽

Alicja Gosiewska ◽

Przemyslaw Biecek

Keyword(s):

R Package ◽

Survival Models

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Sparse network-based regularization for the analysis of patientomics high-dimensional survival data

10.1101/403402 ◽

2018 ◽

Cited By ~ 4

Author(s):

André Veríssimo ◽

Eunice Carrasquinha ◽

Marta B. Lopes ◽

Arlindo L. Oliveira ◽

Marie-France Sagot ◽

...

Keyword(s):

Survival Data ◽

Linear Models ◽

Predictive Performance ◽

R Package ◽

Molecular Data ◽

Data Availability ◽

Survival Models ◽

Centrality Measures ◽

Flexible Tool ◽

Sparse Network

AbstractData availability by modern sequencing technologies represents a major challenge in oncological survival analysis, as the increasing amount of molecular data hampers the generation of models that are both accurate and interpretable. To tackle this problem, this work evaluates the introduction of graph centrality measures in classical sparse survival models such as the elastic net.We explore the use of network information as part of the regularization applied to the inverse problem, obtained both by external knowledge on the features evaluated and the data themselves. A sparse solution is obtained either promoting features that are isolated from the network or, alternatively, hubs, i.e., features that are highly connected within the network.We show that introducing the degree information of the features when inferring survival models consistently improves the model predictive performance in breast invasive carcinoma (BRCA) transcriptomic TCGA data while enhancing model interpretability. Preliminary clinical validation is performed using the Cancer Hallmarks Analytics Tool API and the String database.These case studies are included in the recently released glmSparseNet R package1, a flexible tool to explore the potential of sparse network-based regularizers in generalized linear models for the analysis of omics data.

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Marine halogenated compound analysis: from an R package to the isolation of new griseophenone derivatives

Planta Medica ◽

10.1055/s-0036-1596648 ◽

2016 ◽

Vol 81 (S 01) ◽

pp. S1-S381

Author(s):

C Roullier ◽

Y Guitton ◽

S Prado ◽

O Grovel ◽

YF Pouchus

Keyword(s):

R Package ◽

Halogenated Compound

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The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

10.32942/osf.io/epqa7 ◽

2019 ◽

Author(s):

Shinichi Nakagawa ◽

Malgorzata Lagisz ◽

Rose E O'Dea ◽

Joanna Rutkowska ◽

Yefeng Yang ◽

...

Keyword(s):

Meta Analysis ◽

R Package ◽

Effect Sizes ◽

Forest Plot ◽

Point Estimates ◽

Aggregate Effect ◽

The Individual ◽

Meta Analyses ◽

Heterogeneous Effect ◽

Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

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