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Regarding the data_simulation.Rmd file #280

@zhiiiyang

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@zhiiiyang

Hi, I have a few thoughts about improving the vignette data_simulation.Rmd file for your consideration.

  • add some texts to explain how to convert median survival time to lambda in the create_event_dist function. It would be something like lambda is equal to log(2)/median survival time, e.g., if survival time is equal to 24 months, lambda is set to be log(2)/24.

    exponential_dist <- create_event_dist(dist = "exponential", lambda = 1 / 24)

  • remove the lines associated with external control in the section " Using fixed external data"

    my_data_sim_setup_with_fixed <- create_data_simulation(
    baseline = my_internal_baseline,
    coefficients = c(age = 0.001, score_high = 1.1),
    event_dist = create_event_dist(dist = "exponential", lambdas = 1 / 50),
    fixed_external_data = historical_trial_data
    ) %>%
    set_enrollment(
    internal = enrollment_constant(rate = c(25, 10), for_time = c(4, 30)),
    external = enrollment_constant(rate = c(30, 10), for_time = c(4, 30))
    ) %>%
    set_dropout(
    internal_treated = create_event_dist(dist = "exponential", lambdas = 1 / 50),
    internal_control = create_event_dist(dist = "exponential", lambdas = 1 / 55),
    external_control = create_event_dist(dist = "exponential", lambdas = 1 / 40)
    ) %>%
    set_cut_off(
    internal = cut_off_after_first(time = 60),
    external = cut_off_after_events(n = 100)
    )

  • only count the number of events for cut_off_after_events described in cut off after events #279

    cut_time <- sort(data$enrollment + data$eventtime)[n]

  • Using a cutoff value to dichotomize a continuous variable to a binary variable is not needed since it is pretty straightforward for users to do it instead.

  • I just noticed that you have introduced enrollment_constant function in earlier sections so I think the additional texts are not needed then.

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