npRCT_functions.R

# --------------------------------------------
# --------------npRCT Function----------------
# --------------------------------------------


# --------------t.test eqivalent--------------
npRCT.pwr.t.test <- function(d1 = 0.5, d2 = 0.5, 
                            sig.level1 = 0.05, sig.level2 = 0.05,
                            power1 = 0.8, power2 = 0.8, 
                            type1 = "two.sample", type2 = "two.sample", 
                            alternative1 = "two.sided", alternative2 = "two.sided")    {
    
    # Load required package
    require("pwr")
    
    # Run power calculation for first t-test (traditional part of npRCT)
    t1 <- pwr.t.test(d = d1, sig.level = sig.level1, power = power1, type = type1, 
                     alternative = alternative1)
    
    # Run power calculation for second t-test (stratified part of npRCT)
    t2 <- pwr.t.test(d = d2, sig.level = sig.level2, power = power2, type = type2, 
                     alternative = alternative2)
    
    # Compute sample sizes (per group)
    n1 = ceiling(t1$n)
    n2 = t2$n
    n2_rct = ceiling(n2 / 2) # n still traditionally randomised in stratified part
    
    n_trad = n1 - n2_rct
    n_total = n_trad + n2
    
    # Create output
    output = structure(list(call = "npRCT t.test based Power Calculation",
                            n1 = n1, n2 = n2, d1 = d1, d2 = d2, 
                            sig.level1 = sig.level1, sig.level2 = sig.level2, 
                            power1 = power1, 
                            alternative1 = alternative1,
                            alternative2 = alternative2,
                            note = "Please note that sample sizes are provided for *each* group", 
                            n_trad = n_trad,
                            n_prec = ceiling(n2),
                            n_total = n_total), 
                       class = "npRCT.pwr.test")
    
    # End function by returning the output data.frame
    return(output)
}


# --------------anova eqivalent---------------
npRCT.pwr.anova.test <- function(f1 = 0.25, d2 = 0.5, k = 3,
                             sig.level1 = 0.05, sig.level2 = 0.05,
                             power1 = 0.8, power2 = 0.8, 
                             type2 = "two.sample", alternative2 = "two.sided")    {
    
    # Load required package
    require("pwr")
    
    # Run power calculation for first t-test (traditional part of npRCT)
    anova1 <- pwr.anova.test(k = k, f = f1, sig.level = sig.level1, power = power1)
    
    # Run power calculation for second t-test (stratified part of npRCT)
    t2 <- pwr.t.test(d = d2, sig.level = sig.level2, power = power2, type = type2, 
                     alternative = alternative2)
    
    # Compute sample sizes (per group)
    n1 = ceiling(anova1$n)
    n2 = t2$n
    n2_rct = ceiling(n2 / k) # n still traditionally randomised in stratified part
    
    n_trad = n1 - n2_rct
    n_total = n_trad + n2
    
    # Create output
    output = structure(list(call = "npRCT ANOVA based Power Calculation",
                            n1 = n1, n2 = n2, f1 = f1, d2 = d2, 
                            sig.level1 = sig.level1, sig.level2 = sig.level2, 
                            power1 = power1, power2 = power2,
                            alternative2 = alternative2,
                            n_trad = n_trad,
                            n_prec = ceiling(n2),
                            n_total = n_total), 
                       class = "npRCT.pwr.test")
    
    # End function by returning the output data.frame
    return(output)
}




# --------------quisquare eqivalent----------
npRCT.pwr.chisq.test <- function(w1 = 0.5, w2 = 0.5, 
                                 k = 2, # number of groups
                                 ycat = 2, # number of outcome variable categories
                             sig.level1 = 0.05, sig.level2 = 0.05,
                             power1 = 0.8, power2 = 0.8, 
                             suppressInfo = FALSE)    {
    
    # Load required package
    require("pwr")
    
    # Run power calculation for first chisquare-test (traditional part of npRCT)
    chisq1 <- pwr.chisq.test(w = w1, sig.level = sig.level1, 
                             power = power1, df = (k-1)*(ycat-1))
    
    # Run power calculation for second chisquare-test (stratified part of npRCT)
    chisq2 <- pwr.chisq.test(w = w2, sig.level = sig.level2, 
                             power = power2, df = (ycat-1))
    
    # Compute sample sizes (per group)
    n1 = ceiling(chisq1$N / k)
    n2 = chisq2$N / 2
    n2_rct = ceiling(n2 / k) # n still traditionally randomised in stratified part
    
    n_trad = n1 - n2_rct
    n_total = ceiling(n_trad + n2)
    
    # Create output
    output = structure(list(call = "npRCT chisq test based Power Calculation",
                            n1 = n1, n2 = n2, w1 = w1, w2 = w2, 
                            sig.level1 = sig.level1, sig.level2 = sig.level2, 
                            power1 = power1, power2 = power2,
                            k = k, ycat = ycat,
                            n_trad = n_trad,
                            n_prec = ceiling(n2),
                            n_total = n_total), 
                       class = "npRCT.pwr.test")
    
    
    # End function by returning the output data.frame
    return(output)
    
    
}



# ----------------Summary function----------------

summary.npRCT.pwr.test <- function(x, ...) {
    
    # General summary of sample sizes
    cat("\nCall:\n")
    cat(x$call)
    cat("\n\n")
    cat("Sample sizes of npRCT\n")
    cat("Total n (*across* groups):\n")
    cat(paste0(x$n_total * 2, "\n"))
    cat("\nTraditional RCT n (*each* group):\n")
    cat(paste0(x$n_trad, "\n"))
    cat("\nPrecision RCT n (*each* group):\n")
    cat(paste0(x$n_prec, "\n"))
    cat("\n")
    # Note/ warning if Traditional RCT n is too small
    if(x$n_trad <= 0)   {
        warning("You have obtained a negative sample size for the traditional RCT. This occurs, if the to-be-detected effect of the precision RCT is much smaller than for the traditional RCT (i.e., all participants required for testing the traditional research question (intervention A or B) are recruited as part of the precision RCT. If you want to use the npRCT, please adjust parameters accordingly and visit the Explanations tab for more insights on practical considerations.")
    } else if(x$n_trad <= 20)  {
        cat("Please note that the sample size (per group) of the traditional RCT is relatively small (i.e., smaller than 20). If you aim for online identification of a precision algorithm, you may need to adjust your parameters.")
    }
    
    invisible(x)
}