bdpnormal: Bayesian Discount Prior: Gaussian mean values
In bayesDP: Implementation of the Bayesian Discount Prior Approach for Clinical Trials

bdpnormal

R Documentation

Bayesian Discount Prior: Gaussian mean values

Description

bdpnormal is used for estimating posterior samples from a Gaussian outcome where an informative prior is used. The prior weight is determined using a discount function. This code is modeled after the methodologies developed in Haddad et al. (2017).

Usage

bdpnormal(
  mu_t = NULL,
  sigma_t = NULL,
  N_t = NULL,
  mu0_t = NULL,
  sigma0_t = NULL,
  N0_t = NULL,
  mu_c = NULL,
  sigma_c = NULL,
  N_c = NULL,
  mu0_c = NULL,
  sigma0_c = NULL,
  N0_c = NULL,
  discount_function = "identity",
  alpha_max = 1,
  fix_alpha = FALSE,
  weibull_scale = 0.135,
  weibull_shape = 3,
  number_mcmc = 10000,
  method = "mc",
  compare = TRUE
)

Arguments

`mu_t`	scalar. Mean of the current treatment group.
`sigma_t`	scalar. Standard deviation of the current treatment group.
`N_t`	scalar. Number of observations of the current treatment group.
`mu0_t`	scalar. Mean of the historical treatment group.
`sigma0_t`	scalar. Standard deviation of the historical treatment group.
`N0_t`	scalar. Number of observations of the historical treatment group.
`mu_c`	scalar. Mean of the current control group.
`sigma_c`	scalar. Standard deviation of the current control group.
`N_c`	scalar. Number of observations of the current control group.
`mu0_c`	scalar. Mean of the historical control group.
`sigma0_c`	scalar. Standard deviation of the historical control group.
`N0_c`	scalar. Number of observations of the historical control group.
`discount_function`	character. Specify the discount function to use. Currently supports `weibull`, `scaledweibull`, and `identity`. The discount function `scaledweibull` scales the output of the Weibull CDF to have a max value of 1. The `identity` discount function uses the posterior probability directly as the discount weight. Default value is "`identity`".
`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".
`number_mcmc`	scalar. Number of Monte Carlo simulations. Default is 10000.
`method`	character. Analysis method with respect to estimation of the weight paramter 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 `bdpnormal` vignette `vignette("bdpnormal-vignette", package="bayesDP")` for more details.
`compare`	logical. Should a comparison object be included in the fit? For a one-arm analysis, the comparison object is simply the posterior chain of the treatment group parameter. For a two-arm analysis, the comparison object is the posterior chain of the treatment effect that compares treatment and control. If `compare=TRUE`, the comparison object is accessible in the `final` slot, else the `final` slot is `NULL`. Default is `TRUE`.

Details

bdpnormal uses a two-stage approach for determining the strength of historical data in estimation of a mean outcome. In the first stage, a discount function is used that that defines the maximum strength of the historical data and discounts based on disagreement with the current data. Disagreement between current and historical data is determined by stochastically comparing the respective posterior distributions under noninformative priors. With Gaussian data, the comparison is the proability (p) that the current mean is less than the historical mean. The comparison metric p is then input into the discount function and the final strength of the historical data is returned (alpha).

In the second stage, posterior estimation is performed where the discount function parameter, alpha, is used incorporated in all posterior estimation procedures.

To carry out a single arm (OPC) analysis, data for the current treatment (mu_t, sigma_t, and N_t) and historical treatment (mu0_t, sigma0_t, and N0_t) must be input. The results are then based on the posterior distribution of the current data augmented by the historical data.

To carry our a two-arm (RCT) analysis, data for the current treatment and at least one of current or historical control data must be input. The results are then based on the posterior distribution of the difference between current treatment and control, augmented by available historical data.

For more details, see the bdpnormal vignette:
vignette("bdpnormal-vignette", package="bayesDP")

Value

bdpnormal returns an object of class "bdpnormal". The functions summary and print are used to obtain and print a summary of the results, including user inputs. The plot function displays visual outputs as well.

An object of class bdpnormal is a list containing at least the following components:

posterior_treatment: list. Entries contain values related to the treatment group:

alpha_discount numeric. Alpha value, the weighting parameter of the historical data.
p_hat numeric. The posterior probability of the stochastic comparison between the current and historical data.
posterior_mu vector. A vector of length number_mcmc containing the posterior mean of the treatment group. If historical treatment data is present, the posterior incorporates the weighted historical data.
posterior_sigma2 vector. A vector of length number_mcmc containing the posterior variance of the treatment group. If historical treatment data is present, the posterior incorporates the weighted historical data.
posterior_flat_mu vector. A vector of length number_mcmc containing Monte Carlo samples of the mean of the current treatment group under a flat/non-informative prior, i.e., no incorporation of the historical data.
posterior_flat_sigma2 vector. A vector of length number_mcmc containing Monte Carlo samples of the standard deviation of the current treatment group under a flat/non-informative prior, i.e., no incorporation of the historical data.
prior_mu vector. If historical treatment data is present, a vector of length number_mcmc containing Monte Carlo samples of the mean of the historical treatment group under a flat/non-informative prior.
prior_sigma2 vector. If historical treatment data is present, a vector of length number_mcmc containing Monte Carlo samples of the standard deviation of the historical treatment group under a flat/non-informative prior.

posterior_control: list. Similar entries as posterior_treament. Only present if a control group is specified.
final: list. Contains the final comparison object, dependent on the analysis type:

One-arm analysis: vector. Posterior chain of the mean.
Two-arm analysis: vector. Posterior chain of the mean difference comparing treatment and control groups.

args1: list. Entries contain user inputs. In addition, the following elements are ouput:

arm2 binary indicator. Used internally to indicate one-arm or two-arm analysis.
intent character. Denotes current/historical status of treatment and control groups.

References

Haddad, T., Himes, A., Thompson, L., Irony, T., Nair, R. MDIC Computer Modeling and Simulation working group.(2017) Incorporation of stochastic engineering models as prior information in Bayesian medical device trials. Journal of Biopharmaceutical Statistics, 1-15.

Examples

# One-arm trial (OPC) example
fit <- bdpnormal(
  mu_t = 30, sigma_t = 10, N_t = 50,
  mu0_t = 32, sigma0_t = 10, N0_t = 50,
  method = "fixed"
)
summary(fit)
## Not run: 
plot(fit)

## End(Not run)

# Two-arm (RCT) example
fit2 <- bdpnormal(
  mu_t = 30, sigma_t = 10, N_t = 50,
  mu0_t = 32, sigma0_t = 10, N0_t = 50,
  mu_c = 25, sigma_c = 10, N_c = 50,
  mu0_c = 25, sigma0_c = 10, N0_c = 50,
  method = "fixed"
)
summary(fit2)
## Not run: 
plot(fit2)

## End(Not run)

bayesDP documentation built on March 18, 2022, 7:41 p.m.