DoptBCD {carat} | R Documentation |

`D_A`

-optimal Biased Coin DesignAllocates patients to one of two treatments based on the `D_A`

-optimal biased coin design in the presence of the prognostic factors proposed by Atkinson A C (1982) <doi:10.2307/2335853>.

```
DoptBCD(data)
```

`data` |
a data frame. A row of the dataframe corresponds to the covariate profile of a patient. |

Consider an experiment involving `n`

patients. Assuming a linear model between response and covariates, Atkinson's `D_A`

-optimal biased coin design sequentially assigns patients to minimize the variance of estimated treatment effects. Supposing `j`

patients have been assigned, the probability of assigning the `(j+1)`

th patient to treatment 1 is

`\frac{[1 - (1; \bold{x}^t_{j+1})(\bold{F}^t_j\bold{F}_j)^{-1}\bold{b}_j]^2}{[1-(1; \bold{x}^t_{j+1})(\bold{F}_j^t\bold{F}_j)^{-1}\bold{b}_j]^2+[1 + (1; \bold{x}_{j+1}^t)(\bold{F}_j^t\bold{F}_j)^{-1}\bold{b}_j]^2},`

where `\bold{X} = (\bold{x_i}, i = 1, \dots, j)`

and `\bold{x}_i = (x_{i1}, \dots, x_{ij})`

denote the covariate profile of the `i`

th patient; and `\bold{F}_j = [\bold{1}_j; \bold{X}]`

is the information matrix; and `\bold{b}_j^T=(2\bold{T}_j-\bold{1}_j)^T\bold{F}_j`

, `\bold{T}_j = (T_1, \dots, T_j)`

is a sequence containing the first `j`

patients' allocations.

Details of the procedure can be found in A.C.Atkinson (1982).

It returns an object of `class`

`"carandom"`

.

An object of class `"carandom"`

is a list containing the following components:

`datanumeric` |
a bool indicating whether the data is a numeric data frame. |

`covariates` |
a character string giving the name(s) of the included covariates. |

`strt_num` |
the number of strata. |

`cov_num` |
the number of covariates. |

`level_num` |
a vector of level numbers for each covariate. |

`n` |
the number of patients. |

`Cov_Assig` |
a |

`assignments` |
the randomization sequence. |

`All strata` |
a matrix containing all strata involved. |

`Diff` |
a matrix with only one column. There are final differences at the overall, within-stratum, and within-covariate-margin levels. |

`method` |
a character string describing the randomization procedure to be used. |

`Data Type` |
a character string giving the data type, |

`framework` |
the framework of the used randomization procedure: stratified randomization, or model-based method. |

`data` |
the data frame. |

Atkinson A C. *Optimum biased coin designs for sequential clinical trials with prognostic factors*[J]. Biometrika, 1982, 69(1): 61-67.

See `DoptBCD.sim`

for allocating patients with covariate data generating mechanism.
See `DoptBCD.ui`

for the command-line user interface.

```
# a simple use
## Real Data
df <- data.frame("gender" = sample(c("female", "male"), 100, TRUE, c(1 / 3, 2 / 3)),
"age" = sample(c("0-30", "30-50", ">50"), 100, TRUE),
"jobs" = sample(c("stu.", "teac.", "others"), 100, TRUE),
stringsAsFactors = TRUE)
Res <- DoptBCD(df)
## view the output
Res
## view all patients' profile and assignments
## Res$Cov_Assig
## Simulated Data
n <- 1000
cov_num <- 2
level_num <- c(2, 5)
# Set pr to follow two tips:
#(1) length of pr should be sum(level_num);
#(2)sum of probabilities for each margin should be 1.
pr <- c(0.4, 0.6, rep(0.2, times = 5))
Res.sim <- DoptBCD.sim(n, cov_num, level_num, pr)
## view the output
Res.sim
## view the difference between treatment 1 and treatment 2
## at overall, within-strt. and overall levels
Res.sim$Diff
N <- 5
n <- 100
cov_num <- 2
level_num <- c(3, 5) # << adjust to your CPU and the length should correspond to cov_num
## Set pr to follow two tips:
## (1) length of pr should be sum(level_num);
## (2)sum of probabilities for each margin should be 1
pr <- c(0.3, 0.4, 0.3, rep(0.2, times = 5))
omega <- c(0.2, 0.2, rep(0.6 / cov_num, times = cov_num))
## generate a container to contain Diff
DH <- matrix(NA, ncol = N, nrow = 1 + prod(level_num) + sum(level_num))
DA <- matrix(NA, ncol = N, nrow = 1 + prod(level_num) + sum(level_num))
for(i in 1 : N){
result <- HuHuCAR.sim(n, cov_num, level_num, pr, omega)
resultA <- StrBCD.sim(n, cov_num, level_num, pr)
DH[ , i] <- result$Diff; DA[ , i] <- resultA$Diff
}
## do some analysis
require(dplyr)
## analyze the overall imbalance
Ana_O <- matrix(NA, nrow = 2, ncol = 3)
rownames(Ana_O) <- c("HuHuCAR", "DoptBCD")
colnames(Ana_O) <- c("mean", "median", "95%quantile")
temp <- DH[1, ] %>% abs
tempA <- DA[1, ] %>% abs
Ana_O[1, ] <- c((temp %>% mean), (temp %>% median),
(temp %>% quantile(0.95)))
Ana_O[2, ] <- c((tempA %>% mean), (tempA %>% median),
(tempA %>% quantile(0.95)))
## analyze the within-stratum imbalances
tempW <- DH[2 : (1 + prod(level_num)), ] %>% abs
tempWA <- DA[2 : 1 + prod(level_num), ] %>% abs
Ana_W <- matrix(NA, nrow = 2, ncol = 3)
rownames(Ana_W) <- c("HuHuCAR", "DoptBCD")
colnames(Ana_W) <- c("mean", "median", "95%quantile")
Ana_W[1, ] = c((tempW %>% apply(1, mean) %>% mean),
(tempW %>% apply(1, median) %>% mean),
(tempW %>% apply(1, mean) %>% quantile(0.95)))
Ana_W[2, ] = c((tempWA %>% apply(1, mean) %>% mean),
(tempWA %>% apply(1, median) %>% mean),
(tempWA %>% apply(1, mean) %>% quantile(0.95)))
## analyze the marginal imbalance
tempM <- DH[(1 + prod(level_num) + 1) :
(1 + prod(level_num) + sum(level_num)), ] %>% abs
tempMA <- DA[(1 + prod(level_num) + 1) :
(1 + prod(level_num) + sum(level_num)), ] %>% abs
Ana_M <- matrix(NA, nrow = 2, ncol = 3)
rownames(Ana_M) <- c("HuHuCAR", "DoptBCD")
colnames(Ana_M) <- c("mean", "median", "95%quantile")
Ana_M[1, ] = c((tempM %>% apply(1, mean) %>% mean),
(tempM %>% apply(1, median) %>% mean),
(tempM %>% apply(1, mean) %>% quantile(0.95)))
Ana_M[2, ] = c((tempMA %>% apply(1, mean) %>% mean),
(tempMA %>% apply(1, median) %>% mean),
(tempMA %>% apply(1, mean) %>% quantile(0.95)))
AnaHP <- list(Ana_O, Ana_M, Ana_W)
names(AnaHP) <- c("Overall", "Marginal", "Within-stratum")
AnaHP
```

[Package *carat* version 2.0.2 Index]