Skip to contents

Create a parameter object for the function trimByPs()

Source: R/SettingsObjects.R
createTrimByPsArgs.Rd

Create a parameter object for the function trimByPs()

Usage

createTrimByPsArgs(
  trimFraction = NULL,
  equipoiseBounds = NULL,
  maxWeight = NULL,
  trimMethod = "symmetric"
)

Arguments

trimFraction

For trimFraction = symmetric: the PS cut-off value. For trimFraction = asymmetric or reverse asymmetric: the fraction that will be removed from each treatment group. See trimMethod for more details.

equipoiseBounds

A 2-dimensional numeric vector containing the upper and lower bound on the preference score (Walker, 2013) for keeping persons.

maxWeight

The maximum allowed IPTW.

trimMethod

The trimming method to be performed. Three methods are supported:

  • symmetric: trims all units with estimated PS outside the interval (trimFraction,1-trimFraction), following Crump et al. (2009).

  • asymmetric: removes all units not in the overlap PS range and trims the trimFraction target persons with the lowest propensity scores and comparator persons with the highest propensity scores, following Sturmer et al. (2010).

  • reverse asymmetric: removes all units not in the overlap PS range and trims the trimFraction target persons with the highest propensity scores and comparator persons with the lowest propensity scores (not suggested).

Value

An object of type TrimByPsArgs.

Details

Create an object defining the parameter values. Set any argument to NULL to not use it for trimming.

References

Walker AM, Patrick AR, Lauer MS, Hornbrook MC, Marin MG, Platt R, Roger VL, Stang P, and Schneeweiss S. (2013) A tool for assessing the feasibility of comparative effectiveness research, Comparative Effective Research, 3, 11-20

Crump, Richard K., V. Joseph Hotz, Guido W. Imbens, and Oscar A. Mitnik. 2009. Dealing with limited overlap in estimation of average treatment effects. Biometrika 96(1): 187-199.

Sturmer T, Rothman KJ, Avorn J, Glynn RJ. Treatment effects in the presence of unmeasured confounding: dealing with observations in the tails of the propensity score distribution–a simulation study. Am J Epidemiol. 2010 Oct 1;172(7):843-54.