Propensity Scoring and Beyond: Why? and How?. Midwest Biopharmaceutical Statistics Workshop, 2009. Notation for Variables. y = observed outcome variable(s) x = observed baseline covariate(s) t = observed treatment assignment (usually non-random) z = unobserved explanatory variable(s).
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Midwest Biopharmaceutical Statistics Workshop, 2009
y= observed outcome variable(s)
x= observed baseline covariate(s)
t= observed treatment assignment (usually non-random)
z= unobserved explanatory variable(s)
A fundamental difficulty in observational research is that the probability of treatment assignment, t, is NOT independent of the observed baseline x-covariates. Moreover, these baseline x-covariates are often not ignorable/ancillary. They may of themselves be predictive of y-outcome.
Joint distribution ofxandtgivenp:
Pr( x, t | p ) = Pr( x | p ) Pr( t | p )
i.e. xandtareconditionally independent
propensity for “new” treatment,
p= Pr(t= 1| x ).
The data in the freely distributed “analytical files,” Lsim10K and Lsim5K, used in this session were simulated to be “like” that in an actual OS with only ~1K patients (Lindner Center: Kereiakes et al. Amer Heart J. 2000.)
Unfortunately, many authors and study sponsors do not recognize that sharing their data enhances the credibility of both their study and their analyzes!
The “LSIM10K” dataset contains 10 simulated measurements on 10,325 hypothetical patients.
 mort6mo : Binary 6-month mortality indicator.
 cardcost : Cumulative 6-month cardiac related charges.
 trtm : Binary indicator (1 => treated, 0 => untreated).
 stent : Binary indicator (1 => coronary stent deployment.)
 height : Patient height rounded to the nearest centimeter.
 female : Binary sex indicator (1 => yes, 0 => male.)
 diabetic : Binary indicator (1 => diabetes mellitus, 0 => no.)
 acutemi : Binary indicator (1 => acute myocardial infarction
within the previous 7 days, 0 => no.)
 ejecfrac : Left ejection fraction % rounded to integer.
 ves1proc : Number of vessels involved in initial PCI.
Confounding adjustment: concepts and heuristic ideas. on 10,325 hypothetical patients. Lingling Li, Harvard Medical School and Harvard Pilgrim Health Care
Confounding adjustment: ideas in action – a case study. Xiaochun Li, Div. Biostatistics, IU School of Medicine
The “Local Control” Approach. Bob Obenchain, Risk Benefit Statistics LLC
Gerhardt Pohl, Research Advisor, Lilly USA