Transcript - MBSW Online

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)
Confounding
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.
Propensity Score “Factoring”
Joint distribution of x and t given p:
Pr( x, t | p ) = Pr( x | p ) Pr( t | p )
i.e. x and t are conditionally independent
given the
propensity for “new” treatment,
p = Pr( t = 1 | x ).
Why Go Beyond PS Methods?
1. True PS frequently UNKNOWN.
2. Estimated PS can easily fail to
function the same as true PS.
3. Validating PS Estimates can be
Tedious and Frustrating.
4. Alternatives are worth exploring!
Numerical Example for this Session.
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.
[1] mort6mo : Binary 6-month mortality indicator.
[2] cardcost : Cumulative 6-month cardiac related charges.
[3] trtm : Binary indicator (1 => treated, 0 => untreated).
[4] stent : Binary indicator (1 => coronary stent deployment.)
[5] height : Patient height rounded to the nearest centimeter.
[6] female : Binary sex indicator (1 => yes, 0 => male.)
[7] diabetic : Binary indicator (1 => diabetes mellitus, 0 => no.)
[8] acutemi : Binary indicator (1 => acute myocardial infarction
within the previous 7 days, 0 => no.)
[9] ejecfrac :
Left ejection fraction % rounded to integer.
[10] ves1proc :
Number of vessels involved in initial PCI.
Confounding adjustment: concepts and heuristic
ideas. 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
BREAK
The “Local Control” Approach. Bob Obenchain,
Risk Benefit Statistics LLC
DISCUSSION
Gerhardt Pohl, Research Advisor, Lilly USA