Description Objects from the Class Slots Methods Author(s) References See Also Examples

An encapsulation of the minimization randomization detailed by Pocock and Simon (1975)

Objects can be created by calls of the form
```
new("PocockSimonRandomizer", expt, seed, stateTable, tr.ratios,
d.func, g.func, p.func)
```

. Arguments include a ClinicalExperiment
object, a random number seed, (optionally) a state table of marginal
counts if the randomization is to begin at a particular point in time,
the ratio of treatment counts, a function to use for computing
imbalance for each treatment, a function to compute the overall
imbalance, and a function that provides the probability allocation.

`expt`

:Object of class

`"ClinicalExperiment"`

Defines the clinical experiment context for this randomizer`seed`

:Object of class

`"integer"`

An integer used for seeding the random number generator for reproducibility`stateTable`

:Object of class

`"matrix"`

A matrix of counts indicating the marginal distribution of each factor level per treatment; see example below`tr.assignments`

:Object of class

`"data.frame"`

The treatment assignments so far`tr.ratios`

:Object of class

`"integer"`

The ratio of treatment counts, example 2:1 in a two treatment experiment`d.func`

:Object of class

`"function"`

A function that computes the imbalance; see example below`g.func`

:Object of class

`"function"`

A function that computes the overall imbalance to be minimized; see example below`p.func`

:Object of class

`"function"`

A function that computes the probability vector of treatment assignments; see example below

- computeImbalances
`signature(object = "PocockSimonRandomizer", factor.values = "list")`

: Given a set of factor values associated with a subject, compute imbalances that occur if each of the treatments in turn is assigned to the subject- computeOverallImbalance
`signature(object = "PocockSimonRandomizer", imbalances = "matrix")`

: Given a vector of imbalances resulting from assigning each of the treatments in turn, compute the overall imbalance- initialize
`signature(.Object = "PocockSimonRandomizer")`

: Create an instance of this object- lastRandomization
`signature(object = "PocockSimonRandomizer")`

: Return the details of the last randomization that was done- randomize
`signature(object = "PocockSimonRandomizer", subject.id = "character", factor.values = "character")`

: Given a subject id and a set of factor values, randomize the subject to one of the treatments- stateTable<-
`signature(x = "PocockSimonRandomizer")`

: Set the`stateTable`

slot- tr.assignments<-
`signature(x = "PocockSimonRandomizer")`

: Set the tr.assignments

slot

Balasubramanian Narasimhan

This implementation is based directly on the paper
*Sequential Treatment Assigment with Balancing for Prognostic
Factors in the Controlled Clinical Trial*, by S.~J.~Pocock and
R.~Simon, Biometrics, 31, 103-115

`ClinicalExperiment`

,
`ClinicalExperiment`

class

1 2 3 4 5 6 7 8 9 10 | ```
showClass("PocockSimonRandomizer")
##
## Create a simple PocockSimonRandomizer class
##
expt <- ClinicalExperiment(number.of.factors = 2,
number.of.factor.levels = c(2, 2))
randomizer <- new("PocockSimonRandomizer", expt, as.integer(12345))
randomizer <- randomize(randomizer, "Subject 1", c("1", "2"))
randomizer <- randomize(randomizer, "Subject 2", c("2", "1"))
randomizer
``` |

SRS documentation built on May 31, 2017, 3:18 a.m.

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