In lwjohnst86/PROMISE.desc: Describe Common Statistical Summaries for the PROMISE cohort

Prevalent and incident cases

PROMISE is a prospective cohort of individuals who are followed over time to see who developed type 2 diabetes. So part of describing the dataset is determining prevalence and incidence of diabetes, as well as general dysglycemia disorders (impaired fasting glucose and impaired glucose tolerance). There are other disease/disorders to characterise as well, which the functions explained below will be able to calculate. Before we begin, let's first load up the package as well as the example dataset.

library(PROMISE.desc)
data("example_data")
dplyr::glimpse(example_data)

The first function desc_prevalence calculates prevalent cases of any disease/disorder. We use it by selecting the columns we want to determine prevalent cases. A note, the dataset must contain the columns SID and VN.

prev_data <- example_data %>% 
    desc_prevalence(c("DM", "IFG")) 
prev_data

This function more or less counts disease/disorder status at each time point and re-arranges it into a wide format. If you want to convert this into a table, use:

prev_data %>% 
    desc_table(caption = "*Example* dataset showing prevalence of DM and IFG at each time point.")

For incident cases, you can either add the incidence cases directly to the dataset or describe (count) incident cases like above:

example_data %>% 
    add_incidence(c("DM", "IFG", "IGT"), prefix = "incid_")

This adds the incident data to the original dataset. If you want to describe (count) the incident cases, use:

incid_data <- example_data %>% 
    desc_incidence(c("DM", "IFG"), prefix = "incid_")
incid_data

This function does the same thing as the desc_prevalence function, except it adds the incident cases before and counts these cases. Again, if you want to show this as a table, use:

incid_data %>% 
    desc_table(caption = "*Example* dataset showing incident cases of DM and IFG at each time.")

lwjohnst86/PROMISE.desc documentation built on May 15, 2019, 5:03 a.m.