Calculates Percent Slowing from a Data Frame of Change-from-Baseline Data

Accepts a data frame of change-from-baseline data (probably created with change_from_baseline()) and returns a table of percent slowing results.

Usage

percent_slowing_using_change_from_bl(
  change_from_bl_tbl,
  time_observed_continuous,
  arm,
  control_group,
  subgroup = NULL,
  est = "estimate",
  se = "SE",
  conf.level = 0.95
)

Arguments

change_from_bl_tbl

(data frame)
a data frame of change-from-baseline data whose columns include time_observed_continuous, arm, (optionally) subgroup, est, and se.

time_observed_continuous, arm, subgroup, est, se

(string)
strings identifying the columns in change_from_bl_tbl that contain the continuous time variable, the study arm, (optionally) the subgroup, the change-from-baseline estimate, and the change-from-baseline standard error.

control_group

(string)
the value in the arm column of change_from_bl_tbl denoting the control group.

conf.level

(scalar numeric)
confidence level for the calculation of p-values. Defaults to 0.95.

Value

A data frame with a row for each combination of the unique values of change_from_bl_tbl[[time_observed_continuous]], change_from_bl_tbl[[arm]] (except the value denoted in control_group), and change_from_bl_tbl[[subgroup]] (if subgroup is not NULL). It will contain the following columns:

1. {column name will be the value of the arm argument}: the study arm.

2. {column name will be the value of the time_observed_continuous argument}: the observed continuous time variable.

3. {column name will be the value of the subgroup argument}: the subgroup. Only present if subgroup is not NULL.

4. percent_slowing_est: the percent slowing estimate

5. percent_slowing_lower: the lower bound of the confidence interval for percent_slowing_est.

6. percent_slowing_lower: the upper bound of the confidence interval for percent_slowing_est.

Details

For each study arm that is not the control group,

$$ \text{Let } \theta = \frac{\text{treatment estimate}}{\text{control estimate}} $$$$ \text{Let } \alpha = 1 - \code{conf.level} $$$$ \text{Let MOE} = 100 \times z_{1 - \alpha/2} \times \frac{\sqrt{\text{treatment SE}^2 + (\theta \times \text{control SE})^2}}{|\text{control estimate}|} $$

Therefore, the percent slowing estimates and their respective confidence intervals are calculated thus:

$$ \text{Percent slowing estimate} = (1 - \theta) \times 100 $$$$ \text{Percent slowing CI} = \text{Percent slowing estimate} \pm \text{MOE} $$

Examples

# Create a usable data set out of mmrm::fev_data
fev_mod <- mmrm::fev_data
fev_mod$VISITN <- fev_mod$VISITN * 10
fev_mod$time_cont <- fev_mod$VISITN + rnorm(nrow(fev_mod))
fev_mod$obs_visit_index <- round(fev_mod$time_cont)

fit <-
  ncs_mmrm_fit(
    data = fev_mod,
    type = "subgroup_full",
    response = FEV1,
    subject = USUBJID,
    cov_structs = c("ar1", "us"),
    time_observed_continuous = time_cont,
    df = 2,
    time_observed_index = obs_visit_index,
    time_scheduled_continuous = VISITN,
    arm = ARMCD,
    control_group = "PBO",
    subgroup = SEX,
    subgroup_comparator = "Male",
    covariates = ~ FEV1_BL + RACE
  )

marginal_means <-
  ncs_emmeans(
    fit = fit,
    observed_time = "time_cont",
    scheduled_time = "VISITN",
    arm = "ARMCD",
    subgroup = "SEX"
  )

change_from_bl_tbl <-
  change_from_baseline(
    emmeans = marginal_means,
    time_observed_continuous = "time_cont",
    time_scheduled_baseline = 10,
    arm = "ARMCD",
    subgroup = "SEX",
    as_tibble = TRUE
  )

percent_slowing_using_change_from_bl(
  change_from_bl_tbl = change_from_bl_tbl,
  time_observed_continuous = "time_cont",
  arm = "ARMCD",
  control_group = "PBO",
  subgroup = "SEX"
)
#> # A tibble: 6 × 6
#>   ARMCD time_cont SEX    percent_slowing_est percent_slowing_lower
#>   <fct>     <dbl> <fct>                <dbl>                 <dbl>
#> 1 TRT          20 Male                -45.1                  -95.0
#> 2 TRT          30 Male                -26.2                  -55.9
#> 3 TRT          40 Male                 -8.90                 -43.4
#> 4 TRT          20 Female              -32.3                  -97.1
#> 5 TRT          30 Female              -19.6                  -54.0
#> 6 TRT          40 Female              -10.8                  -45.4
#> # ℹ 1 more variable: percent_slowing_upper <dbl>