oc_stein | R Documentation |
oc_stein()
uses the STEIN design to compute operating charateristics of a user-specificed trial scenario.
This design uses target toxicity and efficacy rates separately to form the cutoff intervals within a decision map.
oc_stein(
ndose,
target_t,
lower_e,
ncohort = 10,
cohortsize = 3,
startdose = 1,
OBD = 0,
psi1 = 0.2,
psi2 = 0.6,
psafe = 0.95,
pfutility = 0.9,
ntrial = 10000,
utilitytype = 1,
u1,
u2,
prob = NULL
)
ndose |
Integer. Number of dose levels. (Required) |
target_t |
Numeric. Target toxicity probability. (Required) |
lower_e |
Numeric. Minimum acceptable efficacy probability. (Required) |
ncohort |
Integer. Number of cohorts. (Default is |
cohortsize |
Integer. Size of a cohort. (Default is |
startdose |
Integer. Starting dose level. (Default is |
OBD |
Integer. True index of the Optimal Biological Dose (OBD) for the trial scenario. (Default is 0)
|
psi1 |
Numerical. Highest inefficacious efficacy probability. |
psi2 |
Numerical. Lowest highly-promising efficacy probability. |
psafe |
Numeric. Early stopping cutoff for toxicity. (Default is |
pfutility |
Numeric. Early stopping cutoff for efficacy. (Default is |
ntrial |
Integer. Number of random trial replications. (Default is |
utilitytype |
Integer. Type of utility structure. (Default is
|
u1 |
Numeric. Utility parameter w_11. (0-100) |
u2 |
Numeric. Utility parameter w_00. (0-100) |
prob |
Fixed probability vectors. If not specified, a random scenario is used by default. Use this parameter to provide fixed probability vectors as a list of the following named elements:
For example: prob <- list( pE = c(0.4, 0.5, 0.6, 0.6, 0.6), pT = c(0.1, 0.2, 0.3, 0.4, 0.4), obd = 3, mtd = 2 ) |
A list containing operating characteristics such as:
OBD selection percentage
Favorable dose selection percentage
Average percentage of patients at the OBD
Average percentage of patients at the favorable doses
Percentage of early stopped trials
Overdose patients percentage
Poor allocation percentage
Overdose selection percentage
oc_stein(
ndose = 5,
target_t = 0.3,
lower_e = 0.4,
ntrial = 10,
)
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