maic (version 0.1.4)

maicMatching: calculate MAIC weights

Description

From index patient level data and a set of target baseline characteristics, calculate MAIC weights.

Usage

maicMatching(
  index,
  target,
  dictionary,
  matching.variables,
  reporting.variables = NULL
)

Arguments

index

A matrix or data.frame containing patient-level data

target

A list containing target summary data

dictionary

A data frame containing the columns "match.id", "target.variable", "index.variable" and "match.type"

matching.variables

A character vector indicating the match.id to use

reporting.variables

A optional character vector of matches to report upon (defaults to matching.variables)

Value

An object of class MaicAnalysis, with components weights and aggregate, containing the weights vector and the covariate aggregate data respectively

Details

The dictionary is a data frame containing at least 4 vectors:

• "match.id" - the name of the match, used to refer to it in the matching.variables list

• "target.variable" - the name of the variable in the target values list use to inform the matching. Use dependent on type

• "index.variable" - the name of the variable in the index data frame to match on.

• "match.type" - A string indicating the match type to use. The following values are accepted:

  • minimum - records with index values lower than the target variable will be discarded

  • maximum - records with index values greater than the target variable will be discarded

  • median - records with index values greater than the target variable will be assigned a value of 1, those lower 0, and those equal 0.5. The target for matching will be a mean of 0.5

  • quantile.X - Generalisation of the median code. records with index values greater than the target variable will be assigned a value of 1, those lower 0. The target for matching will be a mean of 0.X

  • mean - records will match index value directly onto target value

  • proportion - as mean, with index values encoded as 1 = true, 0 = false. If target proportion is exclusive (0 or 1 exactly) then excluded members of the index population shall receive no weighting.

  • sd - a matching on the square of the index value on the sum of the square of the target mean and target standard deviation. The target mean is provided by the "supplementary.target.variable"

  • var - a matching on the square of the index value on the sum of the square of the target mean and the variance specified by the target variable. The target mean is provided by the "supplementary.target.variable"

In addition, the following vector may be necessary:

• "supplementary.target.variable" - The name of the variable in the target values list that provides e.g. the mean for sd and var matching.

It is possible to use these match types to match on other variables, e.g. variance, by pre-processing the input correctly.

Finally, the matching.variables is a list or character vector containing match.ids to be acted upon in this MAIC.

Examples

# NOT RUN {
target <- c("Air.Flow" = 60,
            "Water.Temp" = 21,
            "Prop.Acid.Conc.LT.90" = 0.7,
            "min.air.flow" = 55)

stackloss$match.conc.lt.90 <- 
  ifelse(stackloss$Acid.Conc. < 90, 1, 0)

dict <- data.frame(
  "match.id" = 
    c("airflow", "watertemp", 
      "acidconc", "min.airflow"),
  "target.variable" = 
    c("Air.Flow", "Water.Temp", 
      "Prop.Acid.Conc.LT.90", "min.air.flow"),
  "index.variable" = 
    c("Air.Flow", "Water.Temp", 
      "match.conc.lt.90", "Air.Flow"),
  "match.type" = 
    c("mean", "mean", "proportion", "min"), 
  stringsAsFactors = FALSE)

weightObj <- maicMatching(
  index = stackloss,
  target = target,
  dictionary = dict,
  matching.variables = 
    c("airflow", "watertemp", 
      "acidconc", "min.airflow"))
# }