NU.Learning (version 1.5)

NUcompare: Display NU Sensitivity Graphic for help in choice of K = Number of Clusters

Description

This function displays Box-Whisker diagrams that compare Treatment Effect-Size distributions for different values of K = Number of Clusters requested in X-covariate space. After an initial call to NUsetup(), the analyst typically makes multiple calls to either ltdagg() or lrcagg() for different values of K. The analyst then invokes NUcompare() to see how choice of K changes the location, spread and/or skewness of the distribution of Treatment Effect-Size estimates across Clusters. Variance-Bias trade-offs occur as K increases; large values of K may reduce Bias, but they definitely inflate the Variance of LTD and LRC distributions.

Usage

NUcompare(envir)

Value

NULL

Arguments

envir

R environment output by an earlier call to NUsetup().

Author

Bob Obenchain <[e-mail]>

Details

The third phase of NU.Learning is called EXPLORE and uses graphical Sensitivity Analyses to show how Treatment Effect-Size distributions change with choice of NU parameter settings. Choice of K = Number of Clusters requested is guided, primarily, by NUcompare() graphics. Equally important are the analyst's choices of (i) which [and how many] of the available baseline X-covariates to "adjust for" and (ii) which clustering algorithm and dissimilarity metric to use. Unfortunately, changing these latter choices requires the analyst to essentially "start over" ...i.e. invoking NUcluster() with changed arguments, followed by an invocation of NUsetup() with a different 1st argument. To change only one's choice of y-Outcome variable and/or the Treatment/Exposure variable, a new NUsetup() invocation is all that is needed.

References

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. (2015) NU_Confirm_Guidelines.pdf http://localcontrolstatistics.org

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

Rubin DB. (1980) Bias reduction using Mahalanobis metric matching. Biometrics 36: 293-298.

Tukey JW. (1977) Exploratory Data Analysis, New York: Addison-Wesley, Section 2C.

See Also

ltdagg, ivadj and lrcagg.

Examples

  # \donttest{
  # Running takes more than 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)
  surv100 = ltdagg(100, NU.env)
  surv200 = ltdagg(200, NU.env)
  NUcompare(NU.env)
  # }