UPSaltdd: Artificial Distribution of LTDs from Random Clusters

Description

For a given number of clusters, UPSaltdd() characterizes the potentially biased distribution of "Local Treatment Differences" (LTDs) in a continuous outcome y-variable between two treatment groups due to Random Clusterings. When the NNobj argument is not NA and specifies an existing UPSnnltd() object, UPSaltdd() also computes a smoothed CDF for the NN/LTD distribution for direct comparison with the Artificial LTD distribution.

Usage

UPSaltdd(
  envir,
  dframe,
  trtm,
  yvar,
  faclev = 3,
  scedas = "homo",
  NNobj = NA,
  clus = 50,
  reps = 10,
  seed = 12345
)

Value

dframe: Name of data.frame containing X, t & Y variables.

trtm

Name of treatment factor variable.

yvar

Name of outcome Y variable.

faclev

Maximum number of different numerical values an outcome variable can assume without automatically being converted into a "factor" variable; faclev=1 causes a binary indicator to be treated as a continuous variable determining an average or proportion.

scedas

Scedasticity assumption: "homo" or "hete"

NNobj

Name of an existing UPSnnltd object or NA.

clus

Number of Random Clusters requested per Replication.

reps

Number of overall Replications, each with the same number of requested clusters.

pats

Number of patients with no NAs in their yvar outcome and trtm factor.

seed

Seed for Monte Carlo random number generator.

altdd

Matrix of LTDs and relative weights from artificial clusters.

alxmin

Minimum artificial LTD value.

alxmax

Maximum artificial LTD value.

alymax

Maximum weight among artificial LTDs.

altdcdf

Vector of artificial LTD x-coordinates for smoothed CDF.

Vector of equally spaced CDF values from 0.0 to 1.0.

nnltdd

Optional matrix of relevant NN/LTDs and relative weights.

nnlxmin

Optional minimum NN/LTD value.

nnlxmax

Optional maximum NN/LTD value.

nnlymax

Optional maximum weight among NN/LTDs.

nnltdcdf

Optional vector of NN/LTD x-coordinates for smoothed CDF.

Optional vector of equally spaced CDF values from 0.0 to 1.0.

Arguments

envir: name of the working local control classic environment.
dframe: Name of data.frame containing a treatment-factor and the outcome y-variable.
trtm: Name of treatment factor variable with two levels.
yvar: Name of continuous outcome variable.
faclev: Maximum number of different numerical values an outcome variable can assume without automatically being converted into a "factor" variable; faclev=1 causes a binary indicator to be treated as a continuous variable determining an average or proportion.
scedas: Scedasticity assumption: "homo" or "hete"
NNobj: Name of an existing UPSnnltd object or NA.
clus: Number of Random Clusters requested per Replication; ignored when NNobj is not NA.
reps: Number of overall Replications, each with the same number of requested clusters.
seed: Seed for Monte Carlo random number generator.

Author

Bob Obenchain <wizbob@att.net>

Details

Multiple calls to UPSaltdd() for different UPSnnltd objects or different numbers of clusters are typically made after first invoking UPSgraph().

References

Obenchain RL. (2004) Unsupervised Propensity Scoring: NN and IV Plots. Proceedings of the American Statistical Association (on CD) 8 pages.
Obenchain RL. (2011) USPSinR.pdf USPS R-package vignette, 40 pages.
Rosenbaum PR, Rubin RB. (1983) The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika 70: 41-55.
Rubin DB. (1980) Bias reduction using Mahalanobis metric matching. Biometrics 36: 293-298.