View source: R/groupRAR_functions.R

dyldDBCD_Cont | R Documentation |

Simulating Hu and Zhang's doubly biased coin deisgn with delayed continuous response (number of arms `\ge 2`

) in a clinical trial context. (Inference: two-sided hypothesis testing t-test or chi-square test)

```
dyldDBCD_Cont(n0 = 20, theta, k, ssn, ent.param, rspT.dist, rspT.param,
target.alloc = "Neyman", r = 2, nsim = 2000, mRate = NULL, alpha = 0.05)
```

`n0` |
A positive integer. |

`theta` |
A numerical vector of length equal to |

`k` |
A positive integer. The value specifies the number of treatment groups involved in a clinical trial. ( |

`ssn` |
A positive integer. The value specifies the total number of participants involved in each round of the simulation. |

`ent.param` |
A positive integer. The value specified the parameter for an expoential distribution which determine the time for each participant enter the trial. |

`rspT.dist` |
Distribution Type. Specifies the type of distribution that models the time spent for the availability of patient |

`rspT.param` |
A vector. Specifies the parameters required by the distribution that models the time spent for the availability under each treatment. (eg. If there are 3 treatments groups and each of them follows truncated normal distribution with parameter pair (3, 2), (2, 1), (4, 1), repectively. Then the |

`target.alloc` |
Desired allocation proportion. The option for this argument could be one of |

`r` |
A positive number. Parameter for Hu and Zhang's doubly biased coin design and usually take values 2-4. The default value is 2. |

`nsim` |
a positive integer. The value specifies the total number of simulations, with a default value of 2000. |

`mRate` |
a numerical value between 0 and 1, inclusive, representing the missing rate for the responses. This parameter pertains to missing-at-random data. The default value is |

`alpha` |
a numerical value between 0 and 1. The value represents the predetermined level of significance that defines the probability threshold for rejecting the null hypothesis, with a default value of 0.05. |

Hu and Zhang's Doubly Biased Coin Design with delayed Contiunous Response employs the following treatment allocation scheme:

(a) Initially, due to limited information about treatment efficacy, the first `n0`

patients are assigned to K treatments using restricted randomization (as described by Rosenberger and Lachin, 2002).

(b) For `m \ge n_0`

, patient `(m+1)`

is allocated to treatment `k`

with a probability `p_{m+1, k}`

, which depends on the available responses and estimated target allocation via `g_k`

, as proposed by Hu and Zhang (2004).

For a more comprehensive description of the procedure, please refer to the paper titled 'Doubly adaptive biased coin designs with delayed responses' authored by Hu et al. in 2008.

`name` |
The name of procedure. |

`parameter` |
The true parameters used to do the simulations. |

`assignment` |
The randomization sequence. |

`propotion` |
Average allocation porpotion for each of treatment groups. |

`failRate` |
The average response value for the entire trial. |

`pwClac` |
The probability of the study to detect a significant difference or effect if it truly exists. |

`k` |
Number of arms involved in the trial. |

Hu, F., & Zhang, L. X. (2004). *Asymptotic properties of doubly adaptive biased coin designs for multi-treatment clinical trials.* The Annals of Statistics, 32(1), 268-301.

Hu, F., Zhang, L. X., Cheung, S. H., & Chan, W. S. (2008). *Doubly adaptive biased coin designs with delayed responses.* Canadian Journal of Statistics, 36(4), 541-559.

```
# a simple use
# Define the arguments
## Arguments for generate the simulated data
### For response simulation
theta = c(13, 4.0^2, 15, 2.5^2)
k = 2
ssn = 88
### for enter time and response time simulation
ent.param = 5
rspT.param = rep(10, 2)
rspT.dist = "exponential"
## Arguments for the deisgn
target.alloc = "Neyman"
res = dyldDBCD_Cont(n0 = 10, theta, k, ssn, ent.param, rspT.dist,
rspT.param, target.alloc, r = 2, nsim = 200,
mRate = 0.2, alpha = 0.05)
# View the output (A list of all results)
res
```

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