USPS-package {USPS} | R Documentation |
Define Local Treatment Differences (LTDs) and Local Average Outcomes (LAOs) within Clusters of patients who have been relatively well-matched on their baseline X-covariates. The resulting distribution of LTD effect-size estimates can be interpreted much like a Bayesion posterior yet has been formed, via Nonparametric Preprocessing, in a purely Objective way.
Package: | USPS |
Type: | Package |
Version: | 1.3 |
Date: | 2018-4-2 |
License: | GPL |
SUPERVISED OUTCOME BINNING AND SMOOTHING VIA ESTIMATED PROPENSITY SCORES:
Once one has fitted a somewhat smooth curve through scatters of observed outcomes, Y, versus fitted propensity scores, X, for the patients in each of the two treatment groups, one can consider the question: "Over the range where both smooth curves are defined (i.e. their common support), what is the (weighted) average signed difference between these two curves?"
UNSUPERVISED NEAREST NEIGHBORS / LOCAL TREATMENT DIFFERENCES:
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 (binary) treatment factor t. UPSnnltd(n) then determines the LTD Distribution corresponding to n clusters and, optionally, displays this distribution in a "Snowball" plot.
UNSUPERVISED INSTRUMENTAL VARIABLES / LOCAL AVERAGE y-OUTCOME EFFECTS:
The Observed Propensity Score (OPS) is defined here to be the local (within-cluster) observed fraction of experimental units (patients) choosing the new treatment (t=1) over the control treatment (t=0). Multiple calls to UPSivadj(n) for varying numbers of clusters, n, then yield alternative linear fits to LAO estimates plotted versus their X = OPS. The slope of each such fit determines both the direction and strength of CER preferences for treatment or control.
Bob Obenchain <wizbob@att.net>
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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. (2004) Unsupervised Propensity Scoring: NN and IV Plots. Proceedings of the American Statistical Association (on CD) 8 pages.
Obenchain RL. (2018) USPS_in_R.pdf http://localcontrolstatistics.org 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.
demo(abcix)