pval_cont_fn {reappraised}

Compares observed and expected distribution of p-values for continuous variables

Description

Creates plots of calculated p-value distribution and AUC (area under curve)

Usage

pval_cont_fn(df = pval_cont_data, btsp = 500, title = "", verbose = TRUE)

Arguments

df

data frame generated from load_clean function

btsp

number of bootstrap repeats used to generate 95% confidence interval around AUC

title

optional title for plots

verbose

TRUE or FALSE indicates whether progress bar and comments show and prints plot

Details

Reference data is from (Carlisle 2017, Bolland 2021)
Carlisle JB . Data fabrication and other reasons for non-random sampling in 5087 randomised, controlled trials in anaesthetic and general medical journals. Anaesthesia 2017;72:944–52 .2017
Bolland MJ, Gamble GD, Grey A, Avenell A. Empirically generated reference proportions for baseline p values from rounded summary statistics. Anaesthesia 2020;75:1685-1687.
See also Bolland MJ, Gamble GD, Avenell A, Grey A, Lumley T. Baseline P value distributions in randomized trials were uniform for continuous but not categorical variables. J Clin Epidemiol 2019;112:67-76.
and Bolland MJ, Gamble GD, Avenell A, Grey A. Rounding, but not randomization method, non-normality, or correlation, affected baseline P-value distributions in randomized trials. J Clin Epidemiol 2019;110:50-62.

Returns a list containing 4 objects and (if verbose = TRUE) prints the plot pval_cont_calculated_pvalues

Value

list containing 4 objects as described

• pval_cont_calculated_pvalues = plots of calculated p-value distribution and AUC

• pval_cont_reported_pvalues = plots of reported p-value distribution and AUC (if p-values were reported)

• pval_cont_ft_diff_calc_rep_p = flextable of distribution of differences in calculated and reported results

• all_results = list containing

  • pval_cont_baseline_pvalues_data = data frame of all results used in calculations

  • pval_cont_diff_calc_rep_p = data frame of differences between calculated and reported p-values

  • pval_cont_reported_pvalues= plot of reported p-value distribution

  • pval_cont_auc_reported_pvalues = AUC of reported p-values

  • pval_cont_calculated_pvalues = plot of calculated p-value distribution

  • pval_cont_auc_calculated_pvalues= AUC of calculated p-values

Examples

# load example data
pval_cont_data <- load_clean(import= "no", file.cont = "SI_pvals_cont", pval_cont= "yes",
format.cont = "wide")$pval_cont_data


# run function (takes only a few seconds)
pval_cont_fn(btsp=100)$pval_cont_calculated_pvalues

# to import an excel spreadsheet (modify using local path,
# file and sheet name, range, and format):

# get path for example files
path <- system.file("extdata", "reappraised_examples.xlsx", package = "reappraised",
                    mustWork = TRUE)
# delete file name from path
path <- sub("/[^/]+$", "", path)

# load data
pval_cont_data <- load_clean(import= "yes", pval_cont = "yes", dir = path,
     file.name.cont = "reappraised_examples.xlsx", sheet.name.cont = "SI_pvals_cont",
     range.name.cont = "A1:O51", format.cont = "wide")$pval_cont_data