aba_emmeans: Calculated estimated marginal means.
In aba: Automated Biomarker Analysis

Description Usage Arguments Details Value Examples

This function estimates the estimated marginal means (also known as least-square means) and, if relevant, the treatment effects for mmrm, lme, and ancova models.

1	aba_emmeans(model)

model

abaModel. The fitted aba model to run emmeans on.

This function is based on the emmeans::emmeans function. This function will only be run for the stats which are supported by emmeans.

an abaEmmeans object. This object contains the emmeans, the paired comparisons (i.e., treatment effect), and the sample size at each visit.

# process data: take first 4 visits, only MCI, use CSF abeta as "treatment",
# and create endpoint as change from baseline in cognition at each visit
df <- adnimerge %>%
  dplyr::filter(
    VISCODE %in% c('bl','m06','m12','m24'),
    !is.na(CSF_ABETA_STATUS_bl),
    DX_bl %in% c('MCI')
  ) %>%
  dplyr::mutate(
    TREATMENT = factor(CSF_ABETA_STATUS_bl, levels=c(0,1),
                       labels=c('Placebo','Treatment')),
    ADAS13 = ADAS13 - ADAS13_bl,
    CDRSB = CDRSB - CDRSB_bl,
    MMSE = MMSE - MMSE_bl
  )

# fit mmrm model for different endpoints, adjusted for covariates
model <- df %>% aba_model() %>%
  set_outcomes(CDRSB, ADAS13, MMSE) %>%
  set_covariates(
    AGE, GENDER, EDUCATION
  ) %>%
  set_stats(
    stat_mmrm(id = 'RID', time = 'VISCODE', treatment = 'TREATMENT')
  ) %>%
  aba_fit()

# run emmeans

model_emmeans <- model %>% aba_emmeans()