CODEWITHSUNDEEP
rtidyversefrequencygtsummaryfrequency-distribution
JLA
JLA

Reputation: 13

Tidy data with frequency weights in rows

I have collected non-tidy data from different studies. Assume this data is about studies that report on a number patients having treatment_x and with a treatment outcome in percentages and an x number of recurrences of the treated disease.

library("tidyverse", "gtsummary")

data <- data.frame(
    study_id = c(1, 2, 3, 4, 5),
    no_patients = c(10, 15, 20, 23, 16),
    treatment_id = c("surgery", "radiotherapy", "surgery", "radiotherapy", "surgery"), 
    treatment_outcome = c(0.88, 0.50, 0.90, 0.23, 0.67),
    recurrence = c(0, 2, 4, 3, 6)
)

I want to report this data in a table a compare the different treatment methods

data %>% 
    select(-study_id) %>%
    tbl_summary(by = treatment_id, 
    type = list(no_patients ~ "continuous", recurrence ~ "continuous", treatment_outcome ~ "continuous"),
    statistic = list(
      c("no_patients") ~ "{sum}"
    )) %>%
    add_p()

enter image description here

As the different studies report on different no of patients, they are not of equal importance and study 4 with 23 patients be counted heavier than the other studies with a lower amount of patients.

I could make this table a long format:

data.long <- data[rep(row.names(data), data$no_patients), ]

Now the data has a row per patient, however the total amount of recurrences are attributed to each individual patient. I could also divide the column with recurrences by the amount of patients. However, my actual dataset is way more complicated and has a much higher number of variables.

My questions:

Upvotes: 1

Views: 86

Answers (1)

I_O
I_O

Reputation: 6911

You could group_by treatment_id and use prop.table to obtain groupwise weights:

library(dplyr)

data |>
  group_by(treatment_id) |>
  mutate(treatment_outcome_weighted = treatment_outcome *
           prop.table(no_patients),
         )

Upvotes: 1

Related Questions