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https://github.com/ocbe-uio/bayessurvive
Bayesian survival models for high-dimensional data
https://github.com/ocbe-uio/bayessurvive
bayesian-cox-models bayesian-variable-selection graph-learning high-dimensional-statistics omics-data-integration survival-analysis
Last synced: about 1 month ago
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Bayesian survival models for high-dimensional data
- Host: GitHub
- URL: https://github.com/ocbe-uio/bayessurvive
- Owner: ocbe-uio
- License: gpl-3.0
- Created: 2024-04-19T20:28:56.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-04-20T15:58:39.000Z (8 months ago)
- Last Synced: 2024-04-20T21:39:03.576Z (8 months ago)
- Topics: bayesian-cox-models, bayesian-variable-selection, graph-learning, high-dimensional-statistics, omics-data-integration, survival-analysis
- Language: R
- Homepage:
- Size: 903 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# BayesSurvive
[](https://cran.r-project.org/package=BayesSurvive)
[](https://ocbe-uio.r-universe.dev/BayesSurvive)
[](https://github.com/ocbe-uio/BayesSurvive/actions)
[](https://www.gnu.org/licenses/gpl-3.0)
[](https://doi.org/10.32614/CRAN.package.BayesSurvive)
This is a R/Rcpp package **BayesSurvive** for Bayesian survival models with graph-structured selection priors for sparse identification of high-dimensional features predictive of survival ([Madjar et al., 2021](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04483-z)) (see the three models of the first column in the table below) and its extensions with the use of a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of high-dimensional features (see the three models of the second column in the table below), e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.
Model | Infer `MRF_G` | Fix `MRF_G`
------------:| --------------|---------------
`Pooled` | ✔ | ✔
`CoxBVSSL` | ✔ | ✔
`Sub-struct` | ✔ | ✔
## Installation
Install the latest released version from [CRAN](https://CRAN.R-project.org/package=BayesSurvive)
```r
install.packages("BayesSurvive")
```
Install the latest development version from [GitHub](https://github.com/ocbe-uio/BayesSurvive)
```r
#install.packages("remotes")
remotes::install_github("ocbe-uio/BayesSurvive")
```
## Examples
### Simulate data
```r
library("BayesSurvive")
# Load the example dataset
data("simData", package = "BayesSurvive")
dataset = list("X" = simData[[1]]$X,
"t" = simData[[1]]$time,
"di" = simData[[1]]$status)
```
### Run a Bayesian Cox model
```r
## Initial value: null model without covariates
initial = list("gamma.ini" = rep(0, ncol(dataset$X)))
# Prior parameters
hyperparPooled = list(
"c0" = 2, # prior of baseline hazard
"tau" = 0.0375, # sd (spike) for coefficient prior
"cb" = 20, # sd (slab) for coefficient prior
"pi.ga" = 0.02, # prior variable selection probability for standard Cox models
"a" = -4, # hyperparameter in MRF prior
"b" = 0.1, # hyperparameter in MRF prior
"G" = simData$G # hyperparameter in MRF prior
)
## run Bayesian Cox with graph-structured priors
set.seed(123)
fit <- BayesSurvive(survObj = dataset, model.type = "Pooled", MRF.G = TRUE,
hyperpar = hyperparPooled, initial = initial,
nIter = 200, burnin = 100)
## show posterior mean of coefficients and 95% credible intervals
library("GGally")
plot(fit) +
coord_flip() +
theme(axis.text.x = element_text(angle = 90, size = 7))
```
Show the index of selected variables by controlling Bayesian false discovery rate (FDR) at the level $\alpha = 0.05$
```r
which( VS(fit, method = "FDR", threshold = 0.05) )
```
```
#[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 194
```
### Plot time-dependent Brier scores
The function `BayesSurvive::plotBrier()` can show the time-dependent Brier scores based on posterior mean of coefficients or Bayesian model averaging.
```r
plotBrier(fit, survObj.new = dataset)
```
We can also use the function `BayesSurvive::predict()` to obtain the Brier score at time 8.5, the integrated Brier score (IBS) from time 0 to 8.5 and the index of prediction accuracy (IPA).
```r
predict(fit, survObj.new = dataset, times = 8.5)
```
```{ .text .no-copy }
## Brier(t=8.5) IBS(t:0~8.5) IPA(t=8.5)
## Null.model 0.2290318 0.08185316 0.0000000
## Bayesian.Cox 0.1013692 0.02823275 0.5574011
```
### Predict survival probabilities and cumulative hazards
The function `BayesSurvive::predict()` can estimate the survival probabilities and cumulative hazards.
```r
predict(fit, survObj.new = dataset, type = c("cumhazard", "survival"))
```
```{ .text .no-copy }
# observation times cumhazard survival
##
## 1: 1 3.3 7.41e-05 1.00e+00
## 2: 2 3.3 2.51e-01 7.78e-01
## 3: 3 3.3 9.97e-07 1.00e+00
## 4: 4 3.3 1.84e-03 9.98e-01
## 5: 5 3.3 3.15e-04 1.00e+00
## ---
## 9996: 96 9.5 7.15e+00 7.88e-04
## 9997: 97 9.5 3.92e+02 7.59e-171
## 9998: 98 9.5 2.81e+00 6.02e-02
## 9999: 99 9.5 3.12e+00 4.42e-02
## 10000: 100 9.5 1.97e+01 2.79e-09
```
### Run a 'Pooled' Bayesian Cox model with graphical learning
```r
hyperparPooled <- append(hyperparPooled, list("lambda" = 3, "nu0" = 0.05, "nu1" = 5))
fit2 <- BayesSurvive(survObj = list(dataset), model.type = "Pooled", MRF.G = FALSE,
hyperpar = hyperparPooled, initial = initial, nIter = 10)
```
### Run a Bayesian Cox model with subgroups using fixed graph
```r
# specify a fixed joint graph between two subgroups
hyperparPooled$G <- Matrix::bdiag(simData$G, simData$G)
dataset2 <- simData[1:2]
dataset2 <- lapply(dataset2, setNames, c("X", "t", "di", "X.unsc", "trueB"))
fit3 <- BayesSurvive(survObj = dataset2,
hyperpar = hyperparPooled, initial = initial,
model.type="CoxBVSSL", MRF.G = TRUE,
nIter = 10, burnin = 5)
```
### Run a Bayesian Cox model with subgroups using graphical learning
```r
fit4 <- BayesSurvive(survObj = dataset2,
hyperpar = hyperparPooled, initial = initial,
model.type="CoxBVSSL", MRF.G = FALSE,
nIter = 3, burnin = 0)
```
## References
> Zhi Zhao, Katrin Madjar, Tobias Østmo Hermansen (2024).
> BayesSurvive: Bayesian Survival Models for High-Dimensional Data.
> _R package version 0.0.2_. DOI: [10.32614/CRAN.package.BayesSurvive](https://doi.org/10.32614/CRAN.package.BayesSurvive).
> Katrin Madjar, Manuela Zucknick, Katja Ickstadt, Jörg Rahnenführer (2021).
> Combining heterogeneous subgroups with graph‐structured variable selection priors for Cox regression.
> _BMC Bioinformatics_, 22(1):586. DOI: [10.1186/s12859-021-04483-z](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04483-z).