Historical data for binomial distribution

Wrapper function for historical data from binomial outcome.

historical_binomial(
  y0_treatment = NULL,
  N0_treatment = NULL,
  discount_function = "identity",
  y0_control = NULL,
  N0_control = NULL,
  alpha_max = 1,
  fix_alpha = FALSE,
  weibull_scale = 0.135,
  weibull_shape = 3,
  method = "fixed",
  .data = NULL
)

Arguments

y0_treatment	scalar. Number of events for the historical treatment arm.
N0_treatment	scalar. Number of observations of the historical treatment group.
discount_function	character. If incorporating historical data, specify the discount function. Currently supports the Weibull function (discount_function = "weibull"), the scaled-Weibull function (discount_function = "scaledweibull"), and the identity function (discount_function = "identity"). The scaled-Weibull discount function scales the output of the Weibull CDF to have a maximum value of 1. The identity discount function uses the posterior probability directly as the discount weight. Default value is "identity". See bdpnormal for more details.
y0_control	scalar. Number of events for the historical control arm.
N0_control	scalar. Number of observations of the historical control group.
alpha_max	scalar. Maximum weight the discount function can apply. Default is 1. For a two-arm trial, users may specify a vector of two values where the first value is used to weight the historical treatment group and the second value is used to weight the historical control group.
fix_alpha	logical. Fix alpha at alpha_max? Default value is FALSE.
weibull_scale	scalar. Scale parameter of the Weibull discount function used to compute alpha, the weight parameter of the historical data. Default value is 0.135. For a two-arm trial, users may specify a vector of two values where the first value is used to estimate the weight of the historical treatment group and the second value is used to estimate the weight of the historical control group. Not used when discount_function = "identity".
weibull_shape	scalar. Shape parameter of the Weibull discount function used to compute alpha, the weight parameter of the historical data. Default value is 3. For a two-arm trial, users may specify a vector of two values where the first value is used to estimate the weight of the historical treatment group and the second value is used to estimate the weight of the historical control group. Not used when discount_function = "identity".
method	character. Analysis method with respect to estimation of the weight parameter alpha. Default method "mc" estimates alpha for each Monte Carlo iteration. Alternate value "fixed" estimates alpha once and holds it fixed throughout the analysis. See the the bdpsurvival vignette vignette("bdpsurvival-vignette", package="bayesDP") for more details.
.data	NULL. Stores the proportion of control and treatment. Should not be edited by the user.

Value

A list with historical data for control and treatment group with the discount function.

Examples

historical_binomial(y0_treatment = 5, N0_treatment = 10, y0_control = 15, N0_control = 23)
#> $y0_treatment
#> [1] 5
#> 
#> $N0_treatment
#> [1] 10
#> 
#> $y0_control
#> [1] 15
#> 
#> $N0_control
#> [1] 23
#> 
#> $discount_function
#> [1] "identity"
#> 
#> $alpha_max
#> [1] 1
#> 
#> $fix_alpha
#> [1] FALSE
#> 
#> $weibull_scale
#> [1] 0.135
#> 
#> $weibull_shape
#> [1] 3
#> 
#> $method
#> [1] "fixed"
#> 
historical_binomial(y0_treatment = 5, N0_treatment = 10, y0_control = 15, N0_control = 23,
                     discount_function = "weibull", alpha_max = 1, fix_alpha = FALSE,
                     weibull_scale = 0.135, weibull_shape = 3)
#> $y0_treatment
#> [1] 5
#> 
#> $N0_treatment
#> [1] 10
#> 
#> $y0_control
#> [1] 15
#> 
#> $N0_control
#> [1] 23
#> 
#> $discount_function
#> [1] "weibull"
#> 
#> $alpha_max
#> [1] 1
#> 
#> $fix_alpha
#> [1] FALSE
#> 
#> $weibull_scale
#> [1] 0.135
#> 
#> $weibull_shape
#> [1] 3
#> 
#> $method
#> [1] "fixed"
#>