yppe: Fit the Yang and Prentice Regression Model with Piecewise Exponential baseline distribution.

Usage

yppe(
  formula,
  data,
  n_int = NULL,
  rho = NULL,
  tau = NULL,
  hessian = TRUE,
  approach = c("mle", "bayes"),
  hyper_parms = list(h1_gamma = 0, h2_gamma = 4, mu_psi = 0, sigma_psi = 4, mu_phi = 0,
    sigma_phi = 4, mu_beta = 0, sigma_beta = 4),
  ...
)

Arguments

formula: an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
data: an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which yppe is called.
n_int: number of intervals of the PE distribution. If NULL, default value (square root of n) is used.
rho: the time grid of the PE distribution. If NULL, the function timeGrid is used to compute rho.
tau: the maximum time of follow-up. If NULL, tau = max(time), where time is the vector of observed survival times.
hessian: logical; If TRUE (default), the hessian matrix is returned when approach="mle".
approach: approach to be used to fit the model (mle: maximum likelihood; bayes: Bayesian approach).
hyper_parms: a list containing the hyper-parameters of the prior distributions (when approach = "bayes"). If not specified, default values are used.
...: Arguments passed to either `rstan::optimizing` or `rstan::sampling` .

Value

yppe returns an object of class "yppe" containing the fitted model.

Examples

# \donttest{
# ML approach:
library(YPPE)
mle <- yppe(Surv(time, status)~arm, data=ipass, n_int=10, approach="mle")
summary(mle)
#> Call:
#> yppe(formula = Surv(time, status) ~ arm, data = ipass, n_int = 10, 
#>     approach = "mle")
#> 
#> Short-term coefficients:
#>     Estimate  StdErr z.value   p.value    
#> arm  1.04170 0.16656  6.2541 3.997e-10 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Long-term coefficients:
#>      Estimate    StdErr z.value   p.value    
#> arm -1.238516  0.084555 -14.648 < 2.2e-16 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> --- 
#> loglik = -2759.652   AIC = 5543.305 

# Bayesian approach:
bayes <- yppe(Surv(time, status)~arm, data=ipass, n_int=10, approach="bayes")
#> 
#> SAMPLING FOR MODEL 'yppe' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000726 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 7.26 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 6.721 seconds (Warm-up)
#> Chain 1:                5.941 seconds (Sampling)
#> Chain 1:                12.662 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'yppe' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.000703 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 7.03 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 6.36 seconds (Warm-up)
#> Chain 2:                5.728 seconds (Sampling)
#> Chain 2:                12.088 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'yppe' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 0.000712 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 7.12 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 6.71 seconds (Warm-up)
#> Chain 3:                5.97 seconds (Sampling)
#> Chain 3:                12.68 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'yppe' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 0.000732 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 7.32 seconds.
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 6.908 seconds (Warm-up)
#> Chain 4:                6.151 seconds (Sampling)
#> Chain 4:                13.059 seconds (Total)
#> Chain 4: 
summary(bayes)
#> Call:
#> yppe(formula = Surv(time, status) ~ arm, data = ipass, n_int = 10, 
#>     approach = "bayes")
#> 
#> Short-term coefficients:
#>      mean se_mean    sd  2.5%   25%   50%   75% 97.5%    n_eff Rhat
#> arm 1.039   0.003 0.166 0.722 0.926 1.036 1.151 1.373 2734.998    1
#> 
#> Long-term coefficients:
#>       mean se_mean    sd 2.5%   25%    50%    75%  97.5%    n_eff Rhat
#> arm -1.234   0.002 0.084 -1.4 -1.29 -1.233 -1.178 -1.071 2967.796    1
#> 
#> --- 
#> Inference for Stan model: yppe.
#> 4 chains, each with iter=2000; warmup=1000; thin=1; 
#> post-warmup draws per chain=1000, total post-warmup draws=4000.
#> 
# }