EQUALSTATS (version 0.5.0)

function.Sample_Size_Calculations_Effect_size: Perform Sample Size Calculations using Effect Size Approach

Description

Performs the power and sample size calculations using pwr to calculate the power and sample size for binary and continuous outcomes. It uses ggplot2 to create plots. This functions takes summary information as input. If you want to calculate the sample size based on primary data, use function.Sample_Size_Calculations_Primary.

Usage

function.Sample_Size_Calculations_Effect_size(Predefined_lists, rv)

Value

analysis_outcome

Whether the analysis was performed successfullly

plan

Plan used for analysis

code

Part of code generated for performing the analysis in a standalone version of R

results

Analysis results

results_display

In order to present a single table, multiple results are combined. This results in some numbers stored as text and can cause very wide tables in the shiny output. This issue is fixed wth some modifications to the results table for display purposes.

plots_list

A list of plots generated. Returns "" if no plots are generated.

plots_list_display

In the shiny application, only one figure is displayed. Therefore, a composite image is created from the plots for display purposes. Some analysis functions may return NULL.

selections

Selections made by the user for display.

display_table

Whether the results table should be displayed in the shiny app.

display_plot

Whether the plot should be displayed in the shiny app.

Arguments

Predefined_lists

A list supplied by 'EQUAL-STATS' application

rv

A list supplied by 'EQUAL-STATS' application based on user input

Author

Kurinchi Gurusamy

References

https://sites.google.com/view/equal-group/home

See Also

function.submit_choices function.Sample_Size_Calculations_Primary pwr::pwr.t.test() pwr::pwr.anova.test() pwr::pwr.2p.test() ggplot2::ggplot()

Examples

# Simulate lists provided by EQUAL-STATS ####
Predefined_lists <- list(
  main_menu = c(
    'Calculate summary measures',
    'Create plots',
    'Check distribution',
    'Compare sample mean versus population mean',
    'Compare groups/variables (independent samples)',
    'Compare groups/variables (paired samples or repeated measures)',
    'Find the correlation (quantitative variables)',
    'Calculate measurement error',
    'Find the diagnostic accuracy (primary data)',
    'Perform sample size and power calculations (primary data)',
    'Perform survival analysis',
    'Perform regression analysis',
    'Analyse time series',
    'Perform mixed-effects regression',
    'Perform multivariate regression',
    'Generate hypothesis',
    'Perform sample size and power calculations (effect size approach)',
    'Make correct conclusions (effect size approach)',
    'Find the diagnostic accuracy (tabulated data)'
  ),
  menu_short = c(
    'Summary_Measures',
    'Create_Plots',
    'Check_Distribution',
    'Compare_Sample_Pop_Means',
    'Compare_Groups',
    'Repeated_Measures',
    'Correlation',
    'Measurement_Error',
    'Diagnostic_Accuracy_Primary',
    'Sample_Size_Calculations_Primary',
    'Survival_Analysis',
    'Regression_Analysis',
    'Time_Series',
    'Mixed_Effects_Regression',
    'Multivariate_Regression',
    'Generate_Hypothesis',
    'Sample_Size_Calculations_Effect_size',
    'Make_Conclusions_Effect_size',
    'Diagnostic_Accuracy_Tables'
  )
)
entry <- list()
entry <- lapply(1:15, function(x) entry[[x]] <- '')
rv <- list(
  StorageFolder = tempdir(),
  first_menu_choice = NA,
  second_menu_choice = NA,
  entry = entry,
  import_data = NULL,
  same_row_different_row = NA,
  submit_button_to_appear = FALSE,
  summary_measures_choices = c("EQUAL-STATS choice", "Total observations",
  "Missing observations", "Available observations"),
  analysis_outcome = list(),
  code = list(),
  plan = list(),
  results = list(),
  plots_list = list(),
  reports = list()
)
# Load the necessary packages and functions ####
library(stringr)
library(pwr)
library(ggplot2)
rv$first_menu_choice <- "Sample_Size_Calculations_Effect_size"
rv$second_menu_choice <- "Intervention study (binary outcome)"
rv$entry[[1]] <- 0.5
rv$entry[[2]] <- 0.4
rv$entry[[3]] <- "Intervention is better or worse than comparator"
rv$entry[[4]] <- "Independent samples"
# Final function ####
Results <-  function.Sample_Size_Calculations_Effect_size(Predefined_lists, rv)