LocalControlStrategy (version 1.3.1)

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)

Arguments

x

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

Value

An output list object of class ivadj:

hclobj

Name of clustering object output by LCcluster().

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.

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. (2018) LCstrategy_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 LCcompare.

Examples

# NOT RUN {
  
# }
# NOT RUN {
  # Long running example...
  data(pcidata)
  xvars <- c("stent", "height", "female", "diabetic", "acutemi", "ejfract", "ves1proc")
  hclobj <- LCcluster(pcidata, xvars)
  LC.env <- LCsetup(hclobj, pcidata, thin, surv6mo)
  surv050 <- ltdagg(50)
  iv050 <- ivadj(surv050)
  iv050
  plot(iv050)
  
# }