emee {MRTAnalysis}

Estimates the causal excursion effect for binary outcome MRT

Description

Returns the estimated causal excursion effect (on log relative risk scale) and the estimated standard error. Small sample correction using the "Hat" matrix in the variance estimate is implemented.

Usage

emee(
  data,
  id,
  outcome,
  treatment,
  rand_prob,
  moderator_formula,
  control_formula,
  availability = NULL,
  numerator_prob = NULL,
  start = NULL,
  verbose = TRUE
)

Arguments

data

A data set in long format.

id

The subject id variable.

outcome

The outcome variable.

treatment

The binary treatment assignment variable.

rand_prob

The randomization probability variable.

moderator_formula

A formula for the moderator variables. This should start with ~ followed by the moderator variables. When set to ~ 1, a fully marginal excursion effect (no moderators) is estimated.

control_formula

A formula for the control variables. This should start with ~ followed by the control variables. When set to ~ 1, only an intercept is included as the control variable.

availability

The availability variable. Use the default value (NULL) if your MRT doesn't have availability considerations.

numerator_prob

Either a number between 0 and 1, or a variable name for a column in data. If you are not sure what this is, use the default value (NULL).

start

A vector of the initial value of the estimators used in the numerical solver. If using default value (NULL), a vector of 0 will be used internally. If specifying a non-default value, this needs to be a numeric vector of length (number of moderator variables including the intercept) + (number of control variables including the intercept).

verbose

If default ('TRUE'), additional messages will be printed during data preprocessing.

Value

An object of type "emee_fit"

Examples

## estimating the fully marginal excursion effect by setting
## moderator_formula = ~ 1
emee(
    data = data_binary,
    id = "userid",
    outcome = "Y",
    treatment = "A",
    rand_prob = "rand_prob",
    moderator_formula = ~1,
    control_formula = ~ time_var1 + time_var2,
    availability = "avail"
)

## estimating the causal excursion effect moderated by time_var1
## by setting moderator_formula = ~ time_var1
emee(
    data = data_binary,
    id = "userid",
    outcome = "Y",
    treatment = "A",
    rand_prob = "rand_prob",
    moderator_formula = ~time_var1,
    control_formula = ~ time_var1 + time_var2,
    availability = "avail"
)