R/get.critical.results.G.R

In agaye/ESPRESSO.G: Power Analysis and Sample Size Calculation for a model with a main effect genetic covariate

#' 
#' @title Provides a summary of the main results
#' @description Gets the number of cases and controls or subjects and the 
#' empirical and theoretical power under each model and prints a summary on 
#' the screen.
#' @param scenario scenario number.
#' @param pheno.model outcome type; binary=0 and quantitative=1.
#' @param geno.model genetic model; binary=0 and additive=1.
#' @param sample.sizes.required number of cases and controls or number of subjects 
#' required to achieve the desired power.
#' @param empirical.power estimated empirical power.
#' @param modelled.power calculated theoretical power.
#' @param mean.beta mean beta value of each of the determinants.
#' @return a list that contains the sample size required to achieve the desired power and the
#' @keywords internal
#' estimated modelled and theoretical power.
#' @author Gaye A.

#' 
get.critical.results.G <- function(scenario=1, pheno.model=0, geno.model=1, sample.sizes.required=NULL, 
                                 empirical.power=0.8, modelled.power=0.8, mean.beta=NULL){

  if(is.null(sample.sizes.required)){
    cat("\n\n ALERT!\n")
    cat(" The sample size required may have not been computed.\n")
    cat(" Check the output of the function 'samplsize.calc'.\n")
    stop(" End of process!\n\n", call.=FALSE)
  }
  
  if(is.null(mean.beta)){
    cat("\n\n ALERT!\n")
    cat(" The argument 'mean.beta' is empty.\n")
    cat(" This argument should average beta value'.\n")
    stop(" End of process!\n\n", call.=FALSE)
  }
  
  if(geno.model== 0){
    model <- "binary"
  }else{
    model <- "additive"
  }
  
  if(pheno.model == 0){
    numcases <- sample.sizes.required[[1]]
    numcontrols <- sample.sizes.required[[2]]
  }else{
    numsubjects <- sample.sizes.required[[1]]
  }

  if(pheno.model==0){
     # estimated ORs and effect
     estimated.OR <- exp(mean.beta)
     estimated.effect <- "NA"

					cat("\n---- SUMMARY OF SCENARIO",scenario,"----\n")
					cat("\nModels\n")
					cat("------\n")
					cat("  Outcome: binary \n")
					cat("  Genetic determinant:",model)

					cat("\n\nNumber of cases required\n")
					cat("------------------------\n")
					cat(" ", numcases)

					cat("\n\nNumber of controls required\n")
					cat("---------------------------\n")
					cat(" ", numcontrols)

					cat("\n\nEmpirical power\n")
					cat("---------------\n")
					cat(" ",empirical.power)

					cat("\n\nModel power\n")
					cat("-----------\n")
					cat(" ",round(modelled.power,2))

					cat("\n\nEstimated OR\n")
					cat("-----------\n")
					cat(" ",round(estimated.OR,2))


					cat("\n\n---- END OF SUMMARY ----\n")

		   crit.res <- c(model,numcases,numcontrols,round(empirical.power,2),round(modelled.power,2),
                     round(estimated.OR,2), estimated.effect)
		   return(list(genetic.model=crit.res[1], number.of.cases.required=crit.res[2],
		               number.of.controls.required=crit.res[3],empirical.power=crit.res[4], 
		               modelled.power=crit.res[5],estimated.OR=crit.res[6],estimated.effect=crit.res[[7]]))

  }else{
     # estimated ORs
     estimated.OR <- "NA"
     estimated.effect <- mean.beta
					cat("\n---- SUMMARY OF SCENARIO",scenario,"----\n")
					cat("\nModels\n")
					cat("------\n")
					cat(" Outcome: quantitative \n")
					cat(" Genetic determinant: ",model)

					cat("\n\nNumber of subjects required\n")
					cat("------------------------\n")
					cat(" ",numsubjects)

					cat("\n\nEmpirical power\n")
					cat("---------------\n")
					cat(" ",empirical.power)

					cat("\n\nModel power\n")
					cat("-----------\n")
					cat(" ",round(modelled.power,2))

					cat("\n\nEstimated effect\n")
					cat("-----------\n")
					cat(" ",estimated.effect)

					cat("\n\n---- END OF SUMMARY ----\n")

		   crit.res <- c(model,numsubjects,round(empirical.power,2),round(modelled.power,2),
                     estimated.OR, round(estimated.effect,2))
		   return(list(genetic.model=crit.res[1], number.of.subjects.required=crit.res[2],
		              empirical.power=crit.res[3], modelled.power=crit.res[4], 
                  estimated.OR=crit.res[5], estimated.effect=crit.res[6]))
  }
}