calc_MC_trans_matrix: Transition probability matrix for Bernoulli CUSUM
In success: Survival Control Charts Estimation Software

calc_MC_trans_matrix

R Documentation

Transition probability matrix for Bernoulli CUSUM

Calculates the transition probability matrix for the Bernoulli CUSUM described in Brook & Evans (1972).

calc_MC_trans_matrix(h, n_grid, Wncdf, glmmod, p0, theta, theta_true)

`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.