Enrollment and Follow-Up Monitoring Plots

Enrollment over time is one of the most common clinical study monitoring plots. A cumulative enrollment curve helps investigators see whether recruitment is on track. The following code creates a cumulative enrollment plot: ```r cumulative_enrollment <- prepared_data |> distinct(participant_id, enrollment_date, site) |> arrange(enrollment_date) |> mutate(cumulative_participants = row_number()) cumulative_enrollment |> ggplot(aes(x = enrollment_date, y = cumulative_participants)) + geom_line() + labs( title = "Cumulative Enrollment Over Time", x = "Enrollment date", y = "Cumulative participants" ) ``` If enrollment should be reviewed by site, the plot can use color: ```r site_cumulative_enrollment <- prepared_data |> distinct(participant_id, enrollment_date, site) |> arrange(site, enrollment_date) |> group_by(site) |> mutate(cumulative_participants = row_number()) |> ungroup() site_cumulative_enrollment |> ggplot(aes(x = enrollment_date, y = cumulative_participants, color = site)) + geom_line() + labs( title = "Cumulative Enrollment by Site", x = "Enrollment date", y = "Cumulative participants", color = "Site" ) ``` This chart may become crowded if many sites are included. For many sites, faceting may be better: ```r site_cumulative_enrollment |> ggplot(aes(x = enrollment_date, y = cumulative_participants)) + geom_line() + facet_wrap(~ site) + labs( title = "Cumulative Enrollment by Site", x = "Enrollment date", y = "Cumulative participants" ) ``` Follow-up monitoring can use window status: ```r prepared_data |> count(site, day28_window_status, name = "participants") |> ggplot(aes(x = site, y = participants, fill = day28_window_status)) + geom_col() + coord_flip() + labs( title = "Day 28 Follow-Up Window Status by Site", x = "Site", y = "Participants", fill = "Window status" ) ``` This chart supports action because it shows where visits are overdue, too early, too late, or within window. It should be generated from a clearly defined window rule.