bsgw {BSGW} | R Documentation |

## Bayesian Survival using Generalized Weibull Regression

### Description

Bayesian survival model - with stratification and shrinkage - using Weibull regression on both scale and shape parameters, resulting in time-dependent (i.e. dynamic) hazard ratios.

### Usage

```
bsgw(formula, data, formulas=formula, weights, subset, na.action=na.fail, init="survreg"
, ordweib=FALSE, scale=0, control=bsgw.control(), print.level=2)
bsgw.control(scalex=TRUE, iter=1000, burnin=round(iter/2), sd.thresh=1e-4
, lambda=0.0, lambdas=lambda, nskip=round(iter/10), alpha.min=0.1, alpha.max=10.0
, beta.max=log(20), betas.max=5.0, memlim.gb=8)
## S3 method for class 'bsgw'
print(x, ...)
```

### Arguments

`formula` |
Survival formula expressing the time/status variables as well as covariates used in regression on scale parameter. Currently, only right and left censoring is supported. Must include intercept term. |

`data` |
Data frame containing the covariates and response variable. |

`formulas` |
Formula expressing the covariates used in regression on shape parameter. No left-hand side is necessary since the response variable information is extracted from |

`weights` |
Optional vector of case weights. *Not supported yet* |

`subset` |
Subset of the observations to be used in the fit. *Not supported yet* |

`na.action` |
Missing-data filter function. *Not supported yet (only na.fail behavior works)* |

`init` |
Initialization behavior. Currently, three options are supported: 1) If |

`ordweib` |
If |

`scale` |
If |

`control` |
See |

`print.level` |
Controlling verbosity level. |

`scalex` |
If |

`iter` |
Number of MCMC samples to draw. |

`burnin` |
Number of initial MCMC samples to discard before calculating summary statistics. |

`sd.thresh` |
Threshold for standard deviation of a covariate (after possible centering/scaling). If below the threshold, the corresponding coefficient is removed from sampling, i.e. its value is clamped to zero. |

`lambda` |
Bayesian Lasso shrinkage parameter for scale-parameter coefficients. |

`lambdas` |
Bayesian Lasso shrinkage parameter for shape-parameter coefficients. |

`nskip` |
Controlling how often to print progress report during MCMC run. For example, if |

`alpha.min` |
Lower bound on the shape parameter. |

`alpha.max` |
Upper bound on the shape parameter. |

`beta.max` |
Upper bound on absolute value of coefficients of scale parameter (with the exception of the intercept). |

`betas.max` |
Upper bound on absolute value of coefficients of shape parameter (with the exception of the intercept). |

`memlim.gb` |
User-specified limit on total memory (in GB) available during prediction. Hazard, cumulative hazard, and survival prediction objects are all three-dimensional arrays which can quickly grow very large, depending on data length, number of MCMC samples collected, and number of time points along which prediction is made. |

`x` |
Object of class 'bsgw', usually the result of a call to the |

`...` |
Arguments to be passed to/from other methods. |

### Value

The function `bsgw.control`

returns a list with elements identical to the input parameters. The function `bsgw`

returns an object of class `bsgw`

, with the following components:

`call` |
The matched call. |

`formula` |
Same as input. |

`formulas` |
Same as input. |

`weights` |
Same as input. *Not supported yet* |

`subset` |
Same as input. *Not supported yet* |

`na.action` |
Same as input. *Not supported yet* (current behavior is |

`init` |
Initial values for scale and shape coefficients used in MCMC sampling, either by performing an ordinary Weibull regression or by extracting estimated coefficients from a previously-performed such regression. |

`ordweib` |
Same as input. |

`survreg.scale.ref` |
Value of scale parameter, estimated using ordinary Weibull regression by calling the |

`ordreg` |
The |

`scale` |
Same as input. |

`control` |
Same as input. |

`X` |
Model matrix used for regression on scale parameter, after potential centering and scaling. The corresponding vector of coefficients is called |

`Xs` |
Model matrix used for regression on shape parameter, after potential centering and scaling. The corresponding vector of coefficients is called |

`y` |
Survival response variable (time and status) used in the model. |

`contrasts` |
The contrasts used for scale-parameter coefficients. |

`contrastss` |
The contrasts used for shape-parameter coefficients. |

`xlevels` |
A record of the levels of the factors used in fitting for scale parameter regression. |

`xlevelss` |
A record of the levels of the factors used in fitting for shape parameter regression. |

`terms` |
The terms object used for scale parameter regression. |

`termss` |
The terms object used for shape parameter regression. |

`colnamesX` |
Names of columns for |

`colnamesXs` |
Names of columns for |

`apply.scale.X` |
Index of columns of |

`apply.scale.Xs` |
Index of columns of |

`centerVec.X` |
Vector of centering parameters for columns of |

`scaleVec.X` |
Vector of scaling parameters for columns of |

`centerVec.Xs` |
Vector of centering parameters for columns of |

`scaleVec.Xs` |
Vector of scaling parameters for columns of |

`idx` |
Vector of indexes into |

`idxs` |
Vector of indexes into |

`median` |
List of median values, with elements including |

`smp` |
List of coefficient samples, with the following elements: 1) |

`km.fit` |
Kaplan-Meyer fit to training data. Used in plot.bsgw method. |

`tmax` |
Maximum time value in training set. Used in predict.bsgw for automatic selection of the |

### Author(s)

Alireza S. Mahani, Mansour T.A. Sharabiani

### References

Mazucheli J., Louzada-Neto F. and Achnar J.A. (2002). Lifetime models with nonconstant shape parameters. *Confiabilidade. III Jornada Regional de Estatistica e II Semana da Estatistica, Maringa*.

Neal R.M. (2003). Slice Sampling. *Annals of Statistics*, **31**, 705-767.

Park T. and Casella G. (2008). The Bayesian Lasso. *Journal of the American Statistical Association*, **103**, 681-686.

### See Also

For calculating median and lower/upper bounds on coefficients, use summary.bsgw.

For prediction, use predict.bsgw.

### Examples

```
## model estimation using 800 samples, printing progress every 100 samples
library("survival")
data(ovarian)
est <- bsgw(Surv(futime, fustat) ~ ecog.ps + rx, ovarian
, control=bsgw.control(iter=400, nskip=100))
## comparing shape of Weibull curves between ordinary Weibull and bsgw
## since in bsgw shape is dependent on covariates, only a population average is meaningful
## Note that survreg-style scale is inverse of bsgw shape parameter, see survreg help page
west <- survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian)
cat("constant survreg-style scale parameter:", west$scale, "\n")
cat("population average of survreg-style scale parameter from bsgw model:"
, mean(est$median$survreg.scale), "\n")
```

*BSGW*version 0.9.4 Index]