# minimizePocSim: use the Pocock-Simon or Taves algorithm for computing... In randPack: Randomization routines for Clinical Trials

## Description

use the Pocock-Simon or Taves algorithm for computing covariate-adaptive 'minimization' allocations for a clinical trial

## Usage

 ```1 2``` ```minimizePocSim(df, features, trtvec, obsdf, trttab, f = function(x, y) sum(abs(x + 1 - y))) minimizeTaves(df, features, trtvec, obsdf, trttab) ```

## Arguments

 `df` a data frame with columns corresponding to covariates rows corresponding to subjects `features` character vector of covariates to use `trtvec` vector of assignments made so far `obsdf` data frame for incoming observation, with values for all components enumerated in `features` `trttab` table of treatment ratios `f` score that determines impending allocation

## Details

These functions are generally not called directly. See the vignette; if supplied as the method slot of a MinimizationDesc object the appropriate data are assembled as arguments to these functions.

a treatment code

## Examples

 ```1 2``` ```new("MinimizationDesc", treatments=c(A=1L, B=1L), method=minimizePocSim, type="Minimization", featuresInUse="sex") ```

### Example output

```An object of class "MinimizationDesc"
Slot "method":
function (df, features, trtvec, obsdf, trttab, f = function(x,
y) sum(abs(x + 1 - y)))
{
picks = factorCounts(df, features, trtvec, obsdf)
if (length(picks) > 2)
stop("only works for two-arm studies")
if (length(picks) < 2) {
return(sample(rep(names(trttab), times = trttab), size = 1))
}
sco1 = f(picks[[1]], picks[[2]])
if (length(sco1) != 1 || !is.numeric(sco1))
stop("f must return numeric scalar")
sco2 = f(picks[[2]], picks[[1]])
ans = names(picks)
ifelse(sco1 > sco2, ans[2], ans[1])
}
<environment: namespace:randPack>

Slot "featuresInUse":
[1] "sex"

Slot "treatments":
A B
1 1

Slot "type":
[1] "Minimization"
```

randPack documentation built on Nov. 17, 2017, 12:22 p.m.