aba_model: Create an aba model.
In aba: Automated Biomarker Analysis

Description Usage Arguments Value Examples

An aba model is the foundational object in the aba package. It is composed of the following:

data: a data.frame to be used to fit the statistical models
spec: the specification for the aba model composed of the following:
- groups: subsets of the data
- outcomes: dependent variables in statistical fits.
- covariates: independent variables which should always be included in statistical fits.
- predictors: independent variables which will vary across different statistical fits.
results: the resulting fitted statistics.

aba_model(
  data = NULL,
  groups = NULL,
  outcomes = NULL,
  predictors = NULL,
  covariates = NULL,
  stats = NULL,
  verbose = FALSE
)

`data`	data.frame the data to use for the object
`groups`	vector or list of logical statements as trings. Groups are subsets of the data on which different models will be fit.
`outcomes`	vector or list of strings Outcomes are the dependent variables in the statistical fits.
`predictors`	vector or list of strings Predictors are independent variables which you want to vary. You can include variables on their own or in combination with others. A collection of variables is referred to as a `predictor` and unique variables are referred to as a `term`.
`covariates`	vector of strings Covariates are independent variables which remain fixed across all statistical fits and are therefore always included with the different combinations of predictors.
`stats`	string or abaStat object(s) with `stat_` prefix. Stats are the actual statistical models which you want to fit on the data. Their primary functions are to 1) generate a suitable model formula given the outcome - covariate - predictor combination, and 2) to actually fit the statistical model.
`verbose`	logical. Whether to give a progress bar during model fitting. This can be useful if the fitting procedure is going to take a long time.

An aba model which can be fitted using the aba_fit() function and which can be modified in any manner.

# use built-in data and only take the baseline visit
data <- adnimerge %>% dplyr::filter(VISCODE == 'bl')

# Create aba model w/ data, groups, outcomes, covariates, predictors, stats.
# Note that we start with piping the data into the aba_model... This is
# possible because `data` is the first argument of the `aba_model()` function
# and is useful because it gives auto-completion of variables names in Rstudio.
model <- data %>% aba_model() %>%
  set_groups(everyone(), DX_bl %in% c('MCI','AD')) %>%
  set_outcomes(ConvertedToAlzheimers, CSF_ABETA_STATUS_bl) %>%
  set_covariates(AGE, GENDER, EDUCATION) %>%
  set_predictors(
    PLASMA_ABETA_bl, PLASMA_PTAU181_bl, PLASMA_NFL_bl,
    c(PLASMA_ABETA_bl, PLASMA_PTAU181_bl, PLASMA_NFL_bl)
  ) %>%
  set_stats('glm')

# get a useful view of the model spec:
print(model)

# model specs can be modified to build on one another and save time when
# doing sensitivity analyses. Here, we create the same model as before but
# just add APOE4 as covariate.
model2 <- model %>%
  set_covariates(AGE, GENDER, EDUCATION, APOE4)

# see this change in the model print
print(model2)

# Calling the `fit()` function actually triggers fitting of statistics.
model <- model %>% fit()
model2 <- model2 %>% fit()

# Access the raw results in case you care about that:
print(model$results)

# Calling the `summary()` function summarises covariates and metrics in
# a useful manner
model_summary <- model %>% summary()
model2_summary <- model2 %>% summary()

# see a nicely formatted print out of the summary
print(model_summary)

# or access the raw summary results:
print(model_summary$results)