NU.Learning (version 1.5)

ivadj: Instrumental Variable LAO Fitting and Smoothing

Description

For a given number of patient clusters in baseline X-covariate space and a specified Y-outcome variable, smooth the distribution of Local Average Outcomes (LAOs) plotted versus Within-Cluster Propensity-like Scores: the Treatment Selection Fraction or the Relative Exposure Level.

Usage

ivadj(x)

Value

An output list object of class ivadj:

hclobj

Name of clustering object output by NUcluster().

dframe

Name of data.frame containing X, trtm & Y variables.

trtm

Name of the numeric treatment variable.

yvar

Name of the numeric outcome Y variable.

K

Number of Clusters Requested.

actclust

Number of Clusters actually produced.

Arguments

x

An output object from ltdagg() or lrcagg() using K Clusters in X-covariate space.

Author

Bob Obenchain <[e-mail]>

Details

Multiple invocations of ivadj(ltdagg()) or ivadj(lrgagg()) using varying numbers of clusters, K, can be made. Each invocation of ivadj() displays a linear lm() fit and a smooth.spline() fit to the scatter of LAO estimates plotted versus their within-cluster propensity-like score estimates.

References

McClellan M, McNeil BJ, Newhouse JP. (1994) Does More Intensive Treatment of Myocardial Infarction in the Elderly Reduce Mortality?: Analysis Using Instrumental Variables. JAMA 272: 859-866.

Obenchain RL. (2010) Local Control Approach using JMP. Chapter 7 of Analysis of Observational Health Care Data using SAS, Cary, NC:SAS Press, pages 151-192.

Obenchain RL. (2023) NU.Learning_in_R.pdf http://localcontrolstatistics.org

Rosenbaum PR, Rubin RB. (1983) The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika 70: 41-55.

See Also

ltdagg, lrcagg and NUcompare.

Examples

  # \donttest{
  # Running takes about 7 seconds...
  data(pci15k)
  xvars = c("stent", "height", "female", "diabetic", "acutemi", "ejfract", "ves1proc")
  hclobj = NUcluster(pci15k, xvars)
  NU.env = NUsetup(hclobj, pci15k, thin, surv6mo)
  surv050 = ltdagg(50, NU.env)
  iv050 = ivadj(surv050)
  iv050
  plot(iv050)
  # }