UPSnnltd: Nearest Neighbor Distribution of LTDs in Unsupervised...

UPSnnltdR Documentation

Nearest Neighbor Distribution of LTDs in Unsupervised Propensiy Scoring

Description

For a given number of patient clusters in baseline X-covariate space, UPSnnltd() characterizes the distribution of Nearest Neighbor "Local Treatemnt Differences" (LTDs) on a specified Y-outcome variable.

Usage

UPSnnltd(envir, numclust)

Arguments

envir

name of the working local control classic environment.

numclust

Number of clusters in baseline X-covariate space.

Details

Multiple calls to UPSnnltd(n) for varying numbers of clusters, n, are typically made after first invoking UPShclus() to hierarchically cluster patients in X-space and then invoking UPSaccum() to specify a Y outcome variable and a two-level treatment factor t. UPSnnltd(n) then determines the LTD Distribution corresponding to n clusters and, optionally, displays this distribution in a "Snowball" plot.

Value

An output list object of class UPSnnltd:

  • hiclusName of clustering object created by UPShclus().

  • dframeName of data.frame containing X, t & Y variables.

  • trtmName of treatment factor variable.

  • yvarName of outcome Y variable.

  • numclustNumber of clusters requested.

  • actclustNumber of clusters actually produced.

  • scedasScedasticity assumption: "homo" or "hete"

  • PStdifCharacter string describing the treatment difference.

  • nnhbindfVector containing cluster number for each patient.

  • rawmeanUnadjusted outcome mean by treatment group.

  • rawvarsUnadjusted outcome variance by treatment group.

  • rawfreqNumber of patients by treatment group.

  • ratdifUnadjusted mean outcome difference between treatments.

  • ratsdeStandard error of unadjusted mean treatment difference.

  • binmeanUnadjusted mean outcome by cluster and treatment.

  • binvarsUnadjusted variance by cluster and treatment.

  • binfreqNumber of patients by bin and treatment.

  • awbdifAcross cluster average difference with cluster size weights.

  • awbsdeStandard error of awbdif.

  • wwbdifAcross cluster average difference, inverse variance weights.

  • wwbsdeStandard error of wwbdif.

  • faclevMaximum 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.

  • youtype"contin"uous => only next eight outputs; "factor" => only last three outputs.

  • aovdiffANOVA summary for treatment main effect only.

  • form2Formula for outcome differences due to bins and to treatment nested within bins.

  • bindiffANOVA summary for treatment nested within cluster.

  • sig2Estimate of error mean square in nested model.

  • pbindifUnadjusted treatment difference by cluster.

  • pbinsdeStandard error of the unadjusted difference by cluster.

  • pbinsizCluster radii measure: square root of total number of patients.

  • symsizSymbol size of largest possible Snowball in a UPSnnltd() plot with 1 cluster.

  • factabMarginal table of counts by Y-factor level and treatment.

  • cumchiCumulative Chi-Square statistic for interaction in the three-way, nested table.

  • cumdfDegrees of-Freedom for the Cumulative Chi-Squared.

Author(s)

Bob Obenchain <wizbob@att.net>

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.

See Also

UPSivadj, UPSaccum and UPSgraph.


OHDSI/LocalControl documentation built on Feb. 11, 2024, 9:14 a.m.