Simulate Bayesian Sequential Treatment Comparisons Using a Gaussian Model
gbayesSeqSim(est, asserts)a data frame with number of rows equal to that of est with a number of new columns equal to the number of assertions added. The new columns are named p1, p2, p3, ... (posterior probabilities), mean1, mean2, ... (posterior means), and sd1, sd2, ... (posterior standard deviations). The returned data frame also has an attribute asserts added which is the original asserts augmented with any derived mu and sigma and converted to a data frame, and another attribute alabels which is a named vector used to map p1, p2, ... to the user-provided labels in asserts.
data frame created by estSeqSim()
list of lists. The first element of each list is the user-specified name for each assertion/prior combination, e.g., "efficacy". The other elements are, in order, a character string equal to "<", ">", or "in", a parameter value cutoff (for "<" and ">") or a 2-vector specifying an interval for "in", and either a prior distribution mean and standard deviation named mu and sigma respectively, or a parameter value ("cutprior") and tail area "tailprob". If the latter is used, mu is assumed to be zero and sigma is solved for such that P(parameter > 'cutprior') = P(parameter < - 'cutprior') = tailprob.
Frank Harrell
Simulate a sequential trial under a Gaussian model for parameter estimates, and Gaussian priors using simulated estimates and variances returned by estSeqSim. For each row of the data frame est and for each prior/assertion combination, computes the posterior probability of the assertion.
gbayes(), estSeqSim(), simMarkovOrd(), estSeqMarkovOrd()