Description Usage Arguments Value Examples
This function creates a retest stat object which can be passed as input
to the set_stats()
function when building an aba model. This stat performs
a testretest analysis on data in long format. It can be used to calculate
the bias and variance of biomarkers (or any variables, for that matter)
when measured multiple times. Moreover, the result of a model fit with this
stat can be subsequently passed to the aba_robust()
object in order to
test the effect of testretest bias/variance on clinical prediction models
which can also be fit as an aba model.
1 2 3 4 5 6 7  stat_retest(
id,
time,
method = c("percent_change"),
std.beta = FALSE,
complete.cases = FALSE
)

id 
string. This is the subject id variable in the dataset. This is necessary to keep track of which values belong to which individuals. 
time 
string. This is the time variable in the dataset. This is necessary to keep track of which values belong to which time point. 
method 
string. This is the method used to calculate the difference
between outcome values across time points. Options are:

std.beta 
logical. Whether to standardize the model outcomes and predictors/outcomes prior to analysis. 
complete.cases 
logical. Whether to only include the subset of data with no missing data for any of the outcomes, predictors, or covariates. Note that complete cases are considering within each group  outcome combination but across all predictor sets. 
An abaStat object with retest
stat type.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  # use longitudinal data in healthy controls as pseudo "testretest"
data < adnimerge %>%
dplyr::filter(
VISCODE %in% c('bl' ,'m06', 'm12'),
DX_bl == 'CU'
)
# fit model over two groups and two endpoints
model < data %>% aba_model() %>%
set_groups(
everyone(),
CSF_ABETA_STATUS_bl == 1,
labels = c('CU', 'CU AB')
) %>%
set_outcomes(
ADAS13, MMSE,
labels = c('ADAS13', 'MMSE')
) %>%
set_stats(
stat_retest(id = 'RID', time = 'VISCODE')
) %>%
aba_fit()
# summarise model to get bias and variance estimates
model_summary < model %>% aba_summary()
# plot model results like any other summary
g < model_summary %>% aba_plot_coef(
x='term', group='group', facet=c('outcome','predictor'), coord_flip=TRUE
)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.