calc_MC_trans_matrix {success} | R Documentation |
Transition probability matrix for Bernoulli CUSUM
Description
Calculates the transition probability matrix for the Bernoulli
CUSUM described in Brook & Evans (1972).
Usage
calc_MC_trans_matrix(h, n_grid, Wncdf, glmmod, p0, theta, theta_true)
Arguments
h |
Control limit for the Bernoulli CUSUM
|
n_grid |
Number of state spaces used to discretize the outcome space (when method = "MC" )
or number of grid points used for trapezoidal integration (when method = "SPRT" ).
Increasing this number improves accuracy, but can also significantly increase computation time.
|
glmmod |
Generalized linear regression model used for risk-adjustment as produced by
the function glm() . Suggested:
glm(as.formula("(survtime <= followup) & (censorid == 1) ~ covariates"), data = data) .
Alternatively, a list containing the following elements:
formula :a formula() in the form ~ covariates ;
coefficients :a named vector specifying risk adjustment coefficients
for covariates. Names must be the same as in formula and colnames of data .
|
p0 |
The baseline failure probability at entrytime + followup for individuals.
|
theta |
The \theta value used to specify the odds ratio
e^\theta under the alternative hypothesis.
If \theta >= 0 , the average run length for the upper one-sided
Bernoulli CUSUM will be determined. If \theta < 0 ,
the average run length for the lower one-sided CUSUM will be determined.
Note that
p_1 = \frac{p_0 e^\theta}{1-p_0 +p_0 e^\theta}.
|
theta_true |
The true log odds ratio \theta , describing the
true increase in failure rate from the null-hypothesis. Default = log(1), indicating
no increase in failure rate.
|
[Package
success version 1.1.0
Index]