title	description	free_preview
Introduction to cohorts and to analyzing them	In this chapter we will cover the basics of what a cohort is, an approach to analyzing cohort data, and some first steps in exploring the data. You'll also learn what type of data values to be aware of and to consider when dealing with cohort datasets.	true

Introduction to cohort studies

type: VideoExercise
key: b2111dc061
lang: r
xp: 50
skills: 1

@projector_key fd067459a73b16863b609297f96ac32c

What makes it a cohort?

type: MultipleChoiceExercise
key: ed0789164c
lang: r
xp: 50
skills: 1

The Framingham cohort was set up to study what factors may influence the risk of cardiovascular disease (CVD). People from the town of Framingham, USA were recruited and followed over time. Data was collected on risk factors and CVD outcomes every few years.

The framingham dataset has been loaded for you to optionally explore, however, do note that the dataset has not yet been tidied. We'll go through tidying it in Chapter 2.

What specifically distinguishes the Framingham study as a cohort?

@possible_answers

It studies a disease (CVD).
Participants all came from the town of Framingham, USA.
Participants were followed over time.
Participants had risk factors measured.

@hint

Cohorts are people who have something in common.

@pre_exercise_code

load(url("https://assets.datacamp.com/production/repositories/2079/datasets/8ebd3fc8dc74530ce5a24fe07bca6abf380f9e62/framingham.rda"))
framingham$time <- NULL

@sct

msg1 <- "Almost, but incorrect. Many types of scientific studies study a disease, but that alone doesn't distinguish them as a cohort study."
msg2 <- "Correct! Cohorts are people who share a common characteristic. In this case, the participants come from the same town and so have a similar environment."
msg3 <- "Almost, but incorrect. Many types of scientific studies follow their subjects over time (e.g. clinical trials), but that alone doesn't distinguish them as a cohort study."
msg4 <- "Incorrect. Many types of scientific studies measure risk factors, but that alone doesn't distinguish them as a cohort."
ex() %>% check_mc(2, feedback_msgs = c(msg1, msg2, msg3, msg4))

What cohort type is the Framingham Heart Study?

type: TabExercise
key: f9d57e327c
xp: 100

It's usually possible to determine the cohort design from the variables within the dataset. There are at least two variables in the Framingham Heart study that give us some indication of the cohort design. Recall that cohorts involve a data collection period.

The dplyr package has been loaded, as well as the framingham dataset. Again, note that framingham has not yet been tidied up, which we will do later in the course.

@pre_exercise_code

library(dplyr)
load(url("https://assets.datacamp.com/production/repositories/2079/datasets/8ebd3fc8dc74530ce5a24fe07bca6abf380f9e62/framingham.rda"))
framingham$time <- NULL

@sample_code

# Check out the variable names
names(framingham)

# Select two columns that indicate design
framingham %>% 
    select(_____, _____)

type: NormalExercise
key: fbd1546da1
xp: 50

@instructions

Familiarize yourself with the variables in the framingham dataset.
Select the two variables that give the most indication on framingham's cohort design.

@hint

The Framingham cohort was designed to study the disease cvd.

@sample_code

# Check out the variable names
names(framingham)

# Select the two columns that indicate design
framingham %>% 
    select(___, ___)

@solution

# Check out the variable names
names(framingham)

# Select two columns that indicate design
framingham %>% 
    select(period, cvd)

@sct

success_msg("Great!")

type: MultipleChoiceExercise
key: c22855322d
xp: 50

@question What is Framingham's cohort design? Remember, when the disease occurs is what distinguishes prospective from retrospective cohorts.

@possible_answers

Prospective.
Retrospective.
Neither.
Both.

@hint

The study was designed to investigate how people develop CVD over time (i.e. they don't have the disease when the study starts).

@sct

msg1 = "Correct."
msg2 = "Incorrect. Participants enter the study with a disease. In Framingham, participants did not have the disease."
msg3 = "Incorrect."
msg4 = "Incorrect. It can't be both!"
ex() %>% check_mc(1, feedback_msgs = c(msg1, msg2, msg3, msg4))
success_msg("Nice job! You've identified that period (the time component) and CVD (the disease) tell us that Framingham is a prospective cohort design!")

Cohort types, variables, and the Framingham Study

type: VideoExercise
key: a537fbe14a
lang: r
xp: 50
skills: 1

@projector_key 9e3d8b35b89128ebb91908d3aa815cf1

Select the outcome and some exposures

type: NormalExercise
key: 98e9f6d5d6
lang: r
xp: 100
skills: 1

To properly analyze the data you need to know what each variable represents. Usually it's fairly easy to identify the outcome (the disease). However, knowing which variables are potential exposures to investigate can be tricky, since modern cohort studies often measure hundreds of variables on each participant.

Initially, it can be helpful to keep only the variables of interest. For now, select a few interesting variables, renaming them so they are more descriptive, and exploring them more.

@instructions

Run names(framingham) in the console to find the exact names of the variables.
Choose the correct outcome for cardiovascular disease (CVD). Rename it to got_cvd.
Rename the three predictors to total_cholesterol, body_mass_index and currently_smokes.
Rename the period variable to followup_visit_number.

@hint

Rename bmi to body_mass_index, totchol to total_cholesterol, and cursmoke to currently_smokes.

@pre_exercise_code

library(dplyr)
load(url("https://assets.datacamp.com/production/repositories/2079/datasets/8ebd3fc8dc74530ce5a24fe07bca6abf380f9e62/framingham.rda"))

@sample_code

# Select and rename the potential predictors and outcome
explore_framingham <- framingham %>%
    select(
        # Format: new_variable_name = old_variable_name
        # Outcome
        _____ = cvd,
        # Predictors
        _____ = totchol,
        _____ = bmi,
        _____ = cursmoke,
        # Visit number
        _____ = period 
    )
explore_framingham

@solution

# Select and rename the potential predictors and outcome
explore_framingham <- framingham %>%
    select(
        # Format: new_variable_name = old_variable_name
        # Outcome
        got_cvd = cvd,
        # Predictors
        total_cholesterol = totchol,
        body_mass_index = bmi,
        currently_smokes = cursmoke,
        # Visit number
        followup_visit_number = period 
    )
explore_framingham

@sct

success_msg("Great job! You've selected and renamed the variables correctly.")

Simple summary of the exposures by outcome

type: NormalExercise
key: 45b64907b1
xp: 100

Getting some simple summaries of the exposures by those with and without the disease should be done early in any analysis of cohort datasets. Even more so when there is a time component to the study, so you can identify how variables change over time or are different between groups.

Using what was shown in the video, calculate some means based on some groupings.

@instructions

Group the data by followup_visit_number and got_cvd using the dplyr function group_by().
Calculate the mean for body_mass_index, currently_smokes, and total_cholesterol using summarize() and mean().
Make sure that mean() drops NA values by setting the na.rm argument to TRUE.

@hint

Use na.rm = TRUE with mean() to exclude NA from the mean calculation.

@pre_exercise_code

library(dplyr)
load(url("https://assets.datacamp.com/production/repositories/2079/datasets/8ebd3fc8dc74530ce5a24fe07bca6abf380f9e62/framingham.rda"))
explore_framingham <- framingham %>%
    select(
        got_cvd = cvd,
        total_cholesterol = totchol,
        body_mass_index = bmi,
        currently_smokes = cursmoke,
        followup_visit_number = period
    )

@sample_code

explore_framingham %>% 
    # Group by visit and CVD status
    group_by(___, ___) %>% 
    # Mean of body mass, smoking, and cholesterol
    summarize(
        body_mass_mean = mean(___, na.rm = ___),
        smokes_mean = ___,
        cholesterol_mean = ___
    )

@solution

explore_framingham %>% 
    # Group by visit and CVD status
    group_by(followup_visit_number, got_cvd) %>% 
    # Mean of body mass, smoking, and cholesterol
    summarize(
        body_mass_mean = mean(body_mass_index, na.rm = TRUE),
        smokes_mean = mean(currently_smokes, na.rm = TRUE),
        cholesterol_mean = mean(total_cholesterol, na.rm = TRUE)
    )

@sct

success_msg("Awesome! You learned how to compare the difference in some basic predictors in those who did and did not get CVD over the study duration.")

Prevalence and incidence in cohorts

type: VideoExercise
key: 382b3edde1
lang: r
xp: 50
skills: 1

@projector_key d8b40a3d5d81b2b050f65eb79581aa42

Count number of participants and cases per visit

type: TabExercise
key: 2ba20dff0f
xp: 100

Here, you will count the number of cases and non-cases for both prevalent myocardial infarction (MI), or prevalent_mi, and coronary heart disease (CHD), or prevalent_chd, at each visit. Remember, for longitudinal data, like that in prospective cohorts, you need to count by the time period since each participant will have several rows for each of the data collection visits.

Both dplyr and tidyr are loaded and all variables have been added back into explore_framingham.

@pre_exercise_code

library(dplyr)
load(url("https://assets.datacamp.com/production/repositories/2079/datasets/8ebd3fc8dc74530ce5a24fe07bca6abf380f9e62/framingham.rda"))
explore_framingham <- framingham %>%
    rename(
        got_cvd = cvd, 
        total_cholesterol = totchol,
        body_mass_index = bmi,
        currently_smokes = cursmoke,
        followup_visit_number = period,
        prevalent_chd = prevchd,
        prevalent_mi = prevmi
    )

@sample_code

# Count number of participants per visit
explore_framingham %>%
    count(___)

type: NormalExercise
key: bfd1783eee
xp: 30

@instructions

Use count() to find the number of participants at each followup_visit_number.

@hint

The code is count(followup_visit_number).

@sample_code

# Count number of participants per visit
explore_framingham %>%
    count(___)

@solution

# Count number of participants per visit
explore_framingham %>% 
    count(followup_visit_number)

@sct

success_msg("Great!")

type: NormalExercise
key: c69b3c3b86
xp: 35

@instructions

Count the number of participants with prevalent_mi at each followup_visit_number.

@hint

Include both variables in count(), separated by a comma.

@sample_code

explore_framingham %>% 
    count(followup_visit_number)

# Count by visit, then prevalent cases of MI
explore_framingham %>% 
    count(___, ___)

@solution

explore_framingham %>% 
    count(followup_visit_number)

# Count by visit, then prevalent cases of MI
explore_framingham %>% 
    count(followup_visit_number, prevalent_mi)

@sct

success_msg("Amazing!")

type: NormalExercise
key: f2ddfd6578
xp: 35

@instructions

Lastly, do the same thing for prevalent_chd.

@hint

Use the same syntax as for the prevalent_mi code.

@sample_code

explore_framingham %>% 
    count(followup_visit_number)

explore_framingham %>% 
    count(followup_visit_number, prevalent_mi)

# Count by visit, then prevalent cases of CHD
explore_framingham %>% 
    count(___, ___)

@solution

explore_framingham %>% 
    count(followup_visit_number)

explore_framingham %>% 
    count(followup_visit_number, prevalent_mi)

# Count by visit, then prevalent cases of CHD
explore_framingham %>% 
    count(followup_visit_number, prevalent_chd)

@sct

success_msg("Woohoo! Nice job. You now know how to count the number of cases by visit.")

Remove prevalent cases at the baseline

type: NormalExercise
key: 25d9449073
xp: 100

From the previous exercise, we know that there are prevalent cases of cardiovascular events at the first visit. Prevalent cases of disease at the recruitment visit can introduce bias, so we need to remove these cases before continuing with any further analyses.

@instructions

Exclude (with !) observations where followup_visit_number is equal to 1 and where prevalent_chd is equal to 1.
Count the number of observations to make sure that patients with CHD at the first visit were dropped.

@hint

Filtering logic has the form variable == condition, for instance followup_visit_number == 1.

@pre_exercise_code

library(dplyr)
load(url("https://assets.datacamp.com/production/repositories/2079/datasets/8ebd3fc8dc74530ce5a24fe07bca6abf380f9e62/framingham.rda"))
explore_framingham <- framingham %>%
    rename(
        got_cvd = cvd, 
        total_cholesterol = totchol,
        body_mass_index = bmi,
        participant_age = age,
        currently_smokes = cursmoke,
        followup_visit_number = period,
        prevalent_chd = prevchd,
        prevalent_mi = prevmi
    )

@sample_code

# Drop prevalent chd cases from first visit
no_prevalent_cases <- explore_framingham %>% 
    filter(!(___ == ___ & ___ == ___)) 

# Confirm the number by counting visit then chd cases
no_prevalent_cases %>% 
    count(___, ___)

@solution

# Drop prevalent chd cases from first visit
no_prevalent_cases <- explore_framingham %>% 
    filter(!(followup_visit_number == 1 & prevalent_chd == 1)) 

# Confirm the number by counting visit then chd cases
no_prevalent_cases %>% 
    count(followup_visit_number, prevalent_chd)

@sct

success_msg("Excellent! You've dropped baseline prevalent cases of CHD and started making sure that you've reduced bias in the final results!")

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chapter1.md

chapter1.md

Introduction to cohort studies

What makes it a cohort?

What cohort type is the Framingham Heart Study?

Cohort types, variables, and the Framingham Study

Select the outcome and some exposures

Simple summary of the exposures by outcome

Prevalence and incidence in cohorts

Count number of participants and cases per visit

Remove prevalent cases at the baseline

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chapter1.md

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chapter1.md

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Introduction to cohort studies

What makes it a cohort?

What cohort type is the Framingham Heart Study?

Cohort types, variables, and the Framingham Study

Select the outcome and some exposures

Simple summary of the exposures by outcome

Prevalence and incidence in cohorts

Count number of participants and cases per visit

Remove prevalent cases at the baseline