d estimates the incremental explained risk variation
across a set of pre-specified disease subtypes in a case-control study.
This function takes the name of the disease subtype variable, the number
of disease subtypes, a list of risk factors, and a wide dataset,
and does the needed
transformation on the dataset to get the correct format. Then the polytomous
logistic regression model is fit using
and D is calculated based on the resulting risk predictions.
d(label, M, factors, data)
the name of the subtype variable in the data. This should be a
numeric variable with values 0 through M, where 0 indicates control subjects.
Must be supplied in quotes, e.g.
label = "subtype".
is the number of subtypes. For M>=2.
a list of the names of the binary or continuous risk factors.
For binary risk factors the lowest level will be used as the reference level.
factors = list("age", "sex", "race").
the name of the dataframe that contains the relevant variables.
Begg, C. B., Zabor, E. C., Bernstein, J. L., Bernstein, L., Press, M. F., & Seshan, V. E. (2013). A conceptual and methodological framework for investigating etiologic heterogeneity. Stat Med, 32(29), 5039-5052. doi: 10.1002/sim.5902
d( label = "subtype", M = 4, factors = list("x1", "x2", "x3"), data = subtype_data ) #>  0.4100995