cohort_fn {reappraised}

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