convergence: Assesses the convergence of fitted models for surrogacy...

In surrosurv: Evaluation of Failure Time Surrogate Endpoints in Individual Patient Data Meta-Analyses

Description

This function evaluates whether the fitted models for evaluating the surrogacy of a candidate endpoint have converged. Convergence is assessed by checking whether the maximum gradient is small enough, and whether the Hessian matrix and the variance-covariance matrix of random treatment effects are positive definite.

Usage

  convals(x)
  convergence(x, kkttol = 1e-2, kkt2tol = 1e-8)

Arguments

x	The fitted models, an object of class surrosurv.
kkttol	The tolerance threshold for the assessing whether the maximum (absolute) scaled gradient is small enough.
kkt2tol	The tolerance threshold for checking whether the Hessian matrix and the variance-covariance matrix of random treatment effects are positive definite. The threshold is for the minimum of the eigenvalues.

Value

The function convals() returns a matrix with one row per model and three columans, reporting the values of the maximum scaled gradient (maxSgrad), of the minimum eigenvalue of the Hessian matrix (minHev), and of the minimum eigenvalue of the estimated variance-covariance matrix of random treatment effects (minREev). The function convergence() returns a matrix with the same structure as convals(), with TRUE/FALSE values for the test of the results of convals() against the given thresholds kkttol and kkt2tol.

Author(s)

Federico Rotolo [aut, cre], Xavier Paoletti [ctr], Marc Buyse [ctr], Tomasz Burzykowski [ctr], Stefan Michiels [ctr]

surrosurv documentation built on May 2, 2019, 4:48 p.m.