Several of the statistical methods implemented in package DynTxRegime use propensity score modeling. This section details how this input is to be defined.
For input moPropen, the method specified to obtain predictions
MUST return the prediction on the scale of the probability,
i.e., predictions must be in the range (0,1). In
addition, moPropen differs from standard "modelObj"
objects in that an additional element may be required in
predict.args. Recall, predict.args is the list of control
parameters passed to the prediction method. An additional control
parameter, propen.missing can be included. propen.missing
takes value "smallest" or "largest". It will be required if the
prediction method returns predictions for only a subset of the
treatment data; e.g., predict.glm(). propen.missing indicates if
it is the smallest or the largest treatment value that is missing
from the returned predictions.
For example, fitting a binary treatment (A in {0,1}) using
moPropen <- buildModelObj(model = ~1,
solver.method = 'glm',
solver.args = list('family'='binomial'),
predict.method = 'predict.glm',
predict.args = list(type='response'))
returns only P(A=1). P(A=0) is "missing," and thus
moPropen <- buildModelObj(model = ~1,
solver.method = 'glm',
solver.args = list('family'='binomial'),
predict.method = 'predict.glm',
predict.args = list(type='response',
propen.missing = 'smallest'))
If the dimension of the value returned by the prediction method is
less than the number of treatment options and no value is provided
in propen.missing, it is assumed that the smallest valued treatment
option is missing. Here, 'smallest' indicates the lowest value
integer if treatment is an integer, or the 'base' level if treatment
is a factor.