cohort_fn {reappraised} | R Documentation |
Compares proportions of matching summary statistics in different cohorts
Description
Creates flextable of probability of matching mean, SD, and mean and SD for each variable in different cohorts in the
specified number of simulations
Usage
cohort_fn(
df = cohort_data,
seed = 0,
sims = -1,
n_vars = 10,
popn = "",
title = "",
verbose = TRUE
)
Arguments
df |
data frame generated from load_clean function |
seed |
the seed to use for random number generation, default 0 = current date and time. Specify seed to make repeatable. |
sims |
number of simulations, default -1 = function selects based on number of variables and sample size. |
n_vars |
restrict analyses to variables in at least (>=) this number of cohorts, default = 10 (ie variable has mean in 10 or more cohorts). |
popn |
if dataset contains studies in different sub-populations, code this in cohort_data$population and studies are subsetted if match in this variable. 'All' overrides this and uses all data regardless of information in this variable. |
title |
title name for plots (optional) |
verbose |
TRUE or FALSE indicates whether progress bar and comments show and flextable is printed |
Details
Reference data is from Bolland 2021
Bolland MJ, Gamble GD, Avenell A, Grey A. Identical summary statistics were uncommon in randomized trials and cohort studies. J Clin Epidemiol 2021;136:180-188.
Returns a list containing 6 objects and (if verbose = TRUE) prints the flextable cohort_ft
Value
list containing 6 objects as described
cohort_ft = flextable of results
cohort_graph = plot of observed to expected numbers of matches per cohort for mean; SD; and mean and SD
all_graphs = list containing
all_graphs = all plots on single plot
both_graphs = list of 3 plots row by row used to form all_graphs
individual_graphs= list of 6 individual plots used to form all_graphs
cohort_cohort_data = data frame used to generate results data
cohort_prob_data = data frame used to make flextable
cohort_oe_data= data frame used to make observed to expected plots
Examples
# load example data
cohort_data <- load_clean(import= "no", file.cont = "SI_cohort", cohort= "yes",
format.cont = "long")$cohort_data
# run function (takes close to 5 seconds)
cohort_fn(seed=10, sims = 100)$cohort_ft
# to import an excel spreadsheet (modify using local path,
# file and sheet name, range, and format):
# get path for example files
path <- system.file("extdata", "reappraised_examples.xlsx", package = "reappraised",
mustWork = TRUE)
# delete file name from path
path <- sub("/[^/]+$", "", path)
# load data
cohort_data <- load_clean(import= "yes", cohort = "yes", dir = path,
file.name.cont = "reappraised_examples.xlsx", sheet.name.cont = "SI_cohort",
range.name.cont = "A1:F101", format.cont = "long")$cohort_data