ivadj {NU.Learning} | R Documentation |
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. |
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.
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. |
Author(s)
Bob Obenchain <wizbob@att.net>
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
Examples
# 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)