gs_spending_bound.RdComputes one bound at a time based on spending under given distributional assumptions.
While user specifies gs_spending_bound() for use with other functions,
it is really not intended for use on its own.
Most important user specifications are made through a list provided to functions using gs_spending_bound().
Function uses numerical integration and Newton-Raphson iteration to derive an individual bound for a group sequential
design that satisfies a targeted boundary crossing probability.
Algorithm has been modified from Chapter 19 of Jennison and Turnbull (2000).
gs_spending_bound( k = 1, par = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL), hgm1 = NULL, theta = 0.1, info = 1:3, efficacy = TRUE, test_bound = TRUE, r = 18, tol = 1e-06 )
| k | analysis for which bound is to be computed |
|---|---|
| par | a list with the following items: sf (class spending function), timing (a vector containing values at which spending function is to be evaluated), total_spend (total spend), param (any parameters needed by the spending function) |
| hgm1 | subdensity grid from h1 (k=2) or hupdate (k>2) for analysis k-1; if k=1, this is not used and may be NULL |
| theta | natural parameter used for lower bound only spending; represents average drift at each time of analysis at least up to analysis k; upper bound spending is always set under null hypothesis (theta = 0) |
| info | statistical information at all analyses, at least up to analysis k |
| efficacy | TRUE (default) for efficacy bound, FALSE otherwise |
| test_bound | a logical vector of the same length as |
| r | Integer, at least 2; default of 18 recommended by Jennison and Turnbull |
| tol | Tolerance parameter for convergence (on Z-scale) |
returns a numeric bound (possibly infinite) or, upon failure, generates an error message.
Jennison C and Turnbull BW (2000), Group Sequential Methods with Applications to Clinical Trials. Boca Raton: Chapman and Hall.
Keaven Anderson keaven\_anderson@merck.