Creating Objects of Hierarchical Net-Benefit (Win-Difference) Statistic

netbenefit_ creates an object to be passed into jointCovariance or pated through its ... argument. The object defines a hierarchical net-benefit (a.k.a. win-difference, proportion-in-favor) statistic across a list of endpoints, each declared by nb_tte(), nb_continuous(), or nb_binary().

Usage

netbenefit_(formula, endpoints, data_index = 1)

Arguments

formula

a two-sided R formula <label> ~ arm. The LHS is used purely as a row label in pated() output; the RHS must contain exactly one variable, the column in data holding treatment-arm assignment.

endpoints

a non-empty list of endpoint specs built by nb_tte(), nb_continuous(), or nb_binary(). The order of the list encodes the hierarchical priority: endpoint 1 is checked first; if it ties, the pair falls through to endpoint 2; and so on.

data_index

integer. Index of the data frame in the data argument of jointCovariance to be used.

Value

An object of class c("jc_spec_netbenefit", "jc_spec").

Details

The estimator is $$\widehat\Delta = \frac{N_W - N_L}{N_W + N_L + N_T}$$ where \(N_W\), \(N_L\), and \(N_T\) are the numbers of treatment wins, losses, and overall ties across all \(n_C \times n_T\) pairs (control vs. treatment subject). The per-subject influence function is available in closed form, so both the asymptotic and the bootstrap paths of jointCovariance are supported.

The arm reference level is inferred from the arm column the same way model.matrix(~ arm) would: levels(arm)[1] for factor, the smaller value for numeric or logical, and the alphabetically first value for character. To override, convert the column to a factor with the desired level order before calling netbenefit_().

See also

nb_tte(), nb_continuous(), nb_binary(), jointCovariance(), pated().