Nothing

```
#' @title Analysis wrapper function
#'
#' @description Wrapper function to analyze bayesian trials.
#'
#' @param input list. Input function for all the analysis.
#' @param type character. Type of analysis to be ran (binomial (default),
#' normal. etc.).
#' @param .data NULL. stores the all the details, please do not fill it in.
#'
#' @return a list with results of the analysis of bayesian trial.
#'
#' \describe{
#' \item{\code{prob_of_accepting_alternative}}{
#' scalar. The input parameter of probability of accepting the alternative.}
#' \item{\code{margin}}{
#' scalar. The margin input value of difference between mean estimate of treatment
#' and mean estimate of the control.}
#' \item{\code{alternative}}{
#' character. The input parameter of alternative hypothesis. }
#' \item{\code{N_treatment}}{
#' scalar. The number of patients enrolled in the experimental group for
#' each simulation.}
#' \item{\code{N_control}}{
#' scalar. The number of patients enrolled in the control group for
#' each simulation.}
#' \item{\code{N_enrolled}}{
#' vector. The number of patients enrolled in the trial (sum of control
#' and experimental group for each simulation. )}
#' \item{\code{N_complete}}{
#' scalar. The number of patients who completed the trial and had no
#' loss to follow-up.}
#' \item{\code{post_prob_accept_alternative}}{
#' vector. The final probability of accepting the alternative
#' hypothesis after the analysis is done.}
#' \item{\code{est_final}}{
#' scalar. The final estimate of the difference in posterior estimate of
#' treatment and posterior estimate of the control group.}
#' \item{\code{stop_futility}}{
#' scalar. Did the trial stop for futility during imputation of patient
#' who had loss to follow up? 1 for yes and 0 for no.}
#' \item{\code{stop_expected_success}}{
#' scalar. Did the trial stop for early success during imputation of patient
#' who had loss to follow up? 1 for yes and 0 for no.}
#'
#' }
#'
#' @importFrom stats rbinom glm rnorm lm runif
#' @importFrom dplyr mutate filter group_by bind_rows select n bind_cols
#' @importFrom bayesDP bdpbinomial bdpnormal bdpsurvival
#'
#' @export analysis
#'
analysis <- function(input, type = "binomial", .data = NULL){
if(type == "binomial"){
do.call(binomial_analysis, input)
}
else if(type == "normal"){
do.call(normal_analysis, input)
}
else if(type == "survival"){
do.call(survival_analysis, input)
}
}
```

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