Propensity Scoring and Beyond:
Download
1 / 8

Propensity Scoring and Beyond: Why? and How? - PowerPoint PPT Presentation


  • 302 Views
  • Updated On :

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).

Related searches for Propensity Scoring and Beyond: Why? and How?

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Propensity Scoring and Beyond: Why? and How?' - KeelyKia


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Slide1 l.jpg

Propensity Scoring and Beyond:

Why?andHow?

Midwest Biopharmaceutical Statistics Workshop, 2009


Slide2 l.jpg

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)


Slide3 l.jpg

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.


Slide4 l.jpg

Propensity Score “Factoring”

Joint distribution ofxandtgivenp:

Pr( x, t | p ) = Pr( x | p ) Pr( t | p )

i.e. xandtareconditionally independent

given the

propensity for “new” treatment,

p= Pr(t= 1| x ).


Slide5 l.jpg

Why Go Beyond PS Methods?

  • True PS frequently UNKNOWN.

  • Estimated PS can easily fail to function the same as true PS.

  • Validating PS Estimates can be Tedious and Frustrating.

  • Alternatives are worth exploring!


Slide6 l.jpg

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!


Slide7 l.jpg

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.


Slide8 l.jpg

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

BREAK

The “Local Control” Approach. Bob Obenchain, Risk Benefit Statistics LLC

DISCUSSION

Gerhardt Pohl, Research Advisor, Lilly USA