Description Usage Arguments Details Value Examples

View source: R/FunctionsPoDParameters.R

Function estimates the PoD curve parameters (pmax, slope, et50) using `PoDMLE`

function. Number of PoD curves estimated equals to the repeatCount input parameter.

The estimation is performed using provided diseased and non-diseased subject level data.

1 2 3 4 5 6 7 | ```
PoDParamEstimation(diseasedTiters,
nondiseasedTiters,
nondiseasedGenerationCount,
repeatCount = 500,
adjustTiters = FALSE,
adjustFrom = log2(10),
adjustTo = log2(5))
``` |

`diseasedTiters` |
numeric vector: all diseased titers, subject level data |

`nondiseasedTiters` |
numeric vector: non-diseased titers from immunogenicity subset, subject level data |

`nondiseasedGenerationCount` |
numeric: total number of non-diseased subjects in the clinical trial |

`repeatCount` |
numeric: how many times is the dataset bootstrapped and the PoD curve parameter estimation performed |

`adjustTiters` |
boolean: set to TRUE if titer values should be adjusted, for details see |

`adjustFrom` |
numeric: value specifying the detection limit, all values below the detection limit will be adjusted to adjustTo value |

`adjustTo` |
numeric: value to which titers below the detection limit will be adjusted |

diseasedTiters: subject level titers of all diseased in the clinical trial

nondiseasedTiters: subject level titers of non-diseased subjects in the immunogenicity subset

There are two possible scenarios

Full: Full information about non-diseased titers is available, i.e subject level data for all non-diseased subjects from the clinical trial (nondiseasedGenerationCount = number of all non-diseased subjects in the clinical trial).

Ratio or Fixed: Information about non-diseased titers is available only for the immunogenicity subset. In order to compensate for these missing titers we upsampling of this subset to the total number of non-diseased (nondiseasedGenerationCount) in the trial is needed.

nondiseasedGenerationCount: number of all non-diseased subjects in the clinical trial

NOTE: Number of estimated parameters can be lower than repeatCount as MLE does not necessary converge in all estimations; failcount (number of iterations in which MLE failed to converge) is also returned; for details see `MLE`

function.

Function steps

Upsample non-diseased if needed (needed for methods Ratio and Fixed) - from immunogenicity subset size (N = NondiseasedImmunogenicitySubset$N) to the whole trial size (N = nondiseasedGenerationCount). For details see

`GenerateNondiseased`

function.Estimate PoD curve: resultsPriorReset

Reset disease status: the purpose is to estimate the confidence intervals of the PoD curve and its parameters

Part of the reset disease status procedure is the non-parametric bootstrap: titers of diseased and non-diseased subjects are pooled, and associated PoDs are calculated using their titer values and estimated PoD curve. Based on the subject level probabilities (PoDs), the disease status is reestimated.

Re-estimate PoD curve: new diseased and non-diseased titers are used to reestimate the PoD curve

results: PoD curve parameters after resetting the disease status, named data.frame of estimated PoD curve parameters (pmax, slope, et50); see details for more information

resultsPriorReset: PoD curve parameters prior to resetting the status, named data.frame of estimated PoD curve parameters (pmax, slope, et50); see details for more information

failcount: number of iterations in which MLE failed to converge; see details for more information

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ```
## Data preparation
data(diseased)
data(nondiseased)
## Example 1
# Creating imunogenicity subset, method = "Full"
NondiseasedImmunogenicitySubset <-
ImmunogenicitySubset(diseased,
nondiseased,
method = list(name = "Full",
value = "NA"))
# Number of all non-diseased subjects in the clinical trial
nondiseasedGenerationCount <- nondiseased$N
PoDParamEstimation(diseased$titers,
NondiseasedImmunogenicitySubset$titers,
nondiseasedGenerationCount,
repeatCount = 10)
## Example 2
# Creating imunogenicity subset, method = "Ratio", value = 4
NondiseasedImmunogenicitySubset <-
ImmunogenicitySubset(diseased,
nondiseased,
method = list(name = "Ratio",
value = 4))
# Number of all non-diseased subjects in the clinical trial
nondiseasedGenerationCount <- nondiseased$N
PoDParamEstimation(diseased$titers,
NondiseasedImmunogenicitySubset$titers,
nondiseasedGenerationCount,
repeatCount = 10)
``` |

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