overall_direct {causalPAF} | R Documentation |
Calculation of Population Attributable Fraction (PAF), with a decomposition of the total PAF into direct and indirect components.
Description
Total PAF
Usage
overall_direct(
data_frame,
exposure,
mediator,
response,
mediator_model,
response_model,
response_model_2,
weights
)
Arguments
data_frame |
Data frame containing the data. The data frame has cases in rows and variables in columns. |
exposure |
The exposure name in the form of character string e.g. "phys" for physical exercise. |
mediator |
The mediator name in the form of character string e.g. "whr" for waist hip ratio. |
response |
The outcome name in the form of character string e.g. "case" for a stroke case. |
mediator_model |
A list containing each of the mediator regression models e.g. |
response_model |
is a regression model fitted for the outcome on all mediators together with all parents and confounders of the mediators in a Markov causal Bayesian network DAG. |
response_model_2 |
A regression model fitted for the response in a causal Bayesian network excluding “children” of the exposure in the causal Bayesian network. This regression model will not adjust for mediators (exclude mediators) of the exposure in the regression model so that the total effect of the exposure on the response can be modelled. This model can be listed either as (1) an empty list ( response_model_exposure = list() ) or (2) the user can specify their own customised causal regression model to use. If specified as an empty list, list(), then the 'causalPAF' package will define and fit the model automatically based on the causal DAG defined by the in_outArg parameter. Alternatively, the user can specify the exact model that the user wishes to use, this model must be in list format (list() where length(response_model_exposure) == 1 ), of length 1, assuming only one exposure of interest (other exposures can be risk factors) and the model must be defined within a list() since the package assumes a list() format is supplied. See example in tutorial. E.G. If physical exercise ("exer") in the example given in the diagram is the exposure. Then the regression would include all parents of "exer" (i.e. sex, region, educ, age) as well as risk factors at the same level of the causal Bayesian network (i.e. stress, smoke, diet, alcoh). |
weights |
A numeric |
Value
directPAF |
direct PAF |