box.psa: Compare balance graphically of a continuous covariate as part of a PSA

Description

Given predefined strata and two level treatment for a continuous covariate from a propensity score analysis, box.psa draws pairs of side by side boxplots corresponding to control and treatment for each stratum.

Usage

box.psa(
  continuous,
  treatment = NULL,
  strata = NULL,
  boxwex = 0.17,
  offset = 0.17,
  col = c("yellow", "orange", "black", "red", "darkorange3"),
  xlab = "Stratum",
  legend.xy = NULL,
  legend.labels = NULL,
  pts = TRUE,
  balance = FALSE,
  trim = 0,
  B = 1000,
  ...
)

Arguments

continuous: Vector or N X 3 dataframe or matrix. If a vector, then represents the quantitative covariate that is being balanced within strata in a PSA. If continuous has three columns, then the second and third are assumed to be the treatment and strata respectively. Missing values are not allowed.
treatment: Binary vector of same length as continuous representing the two treatments; can be a character vector or factor.
strata: A vector or factor of same length as continuous indicating the derived strata from estimated propensity scores. May be numeric or character vector, or factor. Strata are ordered lexicographically in plot.
boxwex: Numeric; controls width of boxes. Default = 0.17
offset: Numeric; controls distance between the two boxes in each stratum. Default = 0.17
col: Default = c("yellow", "orange", "black", "red", "darkorange3"). Color vector for the control boxes, treatment boxes, and line connecting their means.
xlab: Label for the x-axis; default = "Stratum". Other standard labels may be used as well.
legend.xy: Binary vector giving coordinates of the legend. By default the legend is placed to the top left.
legend.labels: Vector of labels for the legend; default is essentially c("Treatment (first)", "Treatment (second)", "Treatment Means Compared", "KS p-values", "Strata-Treatment Size") where treatment names are taken from treatment. Vector has four elements if balance = FALSE, ommitting "KS p-values".
pts: Logical; if TRUE then (jittered) points are added on top of the boxplots.
balance: Logical; if TRUE then bal.ms.psa provides a histogram of a permutation distribution and reference statstic to assess balance across strata; bal.ks.psa adds p-values to the graph derived from 2-sample Kologmorov-Smirnov tests of equivalence of control/treatment distributions within each stratum.
trim: If balance=TRUE, defines fraction (0 to 0.5) of observations to be trimmed from each end of stratum-treatment level before the mean is computed. See mean, bal.ms.psa.
B: Passed to bal.ms.psa if necessary, determines number of randomly generated comparison statistics. Default =1000.
...: Other graphical parameters passed to boxplot.

Author

James E. Helmreich James.Helmreich@Marist.edu

Robert M. Pruzek RMPruzek@yahoo.com

Details

Draws a pair of side by side boxplots for each stratum of a propensity score analysis. This allows visual comparisons within strata of the distribution of the given continuous covariate, and comparisons between strata as well. The number of observations in each boxplot are given below each box, and the means of paired treatment and control groups are connected.

Examples

Run this code


continuous<-rnorm(1000)
treatment<-sample(c(0,1),1000,replace=TRUE)
strata<-sample(5,1000,replace=TRUE)
box.psa(continuous, treatment, strata)

data(lindner)
attach(lindner)
lindner.ps <- glm(abcix ~ stent + height + female +
      diabetic + acutemi + ejecfrac + ves1proc,
      data = lindner, family = binomial)
ps<-lindner.ps$fitted
lindner.s5 <- as.numeric(cut(ps, quantile(ps, seq(0, 1, 1/5)),
      include.lowest = TRUE, labels = FALSE))
box.psa(ejecfrac, abcix, lindner.s5, xlab = "ejecfrac",
      legend.xy = c(3.5,110))

lindner.s10 <- as.numeric(cut(ps, quantile(ps, seq(0, 1, 1/5)),
      include.lowest = TRUE, labels = FALSE))
box.psa(height, abcix, lindner.s10, xlab="height",
      boxwex = .15, offset = .15, legend.xy = c(2,130), balance = TRUE)