[R] propensity scores & imputation

[David Paul]

Hi Mr. Gunter,

Will do.  Thanks, I've not visited stats.stackexchange before.


Kind Regards,

	David

-----Original Message-----
From: Bert Gunter [mailto:bgunter.4567 at gmail.com] 
Sent: Thursday, March 16, 2017 7:51 PM
To: david.paul at statmetrics.biz
Cc: R-help <r-help at r-project.org>
Subject: Re: [R] propensity scores & imputation

Way out of bounds for this list (see the posting guide). Try posting on stats.stackexchange.com instead.

Cheers,
Bert


Bert Gunter

"The trouble with having an open mind is that people keep coming along and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )


On Thu, Mar 16, 2017 at 10:42 AM, David Paul <david.paul at statmetrics.biz> wrote:
> Hi,
>
>
>
> Many thanks in advance for whatever advice / input I may receive.
>
>
>
> I have a propensity score matching / data imputation question.  The 
> purpose of the propensity
>
> score modeling is to put subjects from two different clinical trials 
> on a similar footing so that a key
>
> clinical measurement from one study can be attributed / imputed to the 
> other study.  The goal is
>
> NOT to directly compare the two studies, so this is a very atypical 
> kind of propensity score usage.
>
>
>
> I am using lrm( ) to obtain estimated propensity scores, and my 
> question to this List is rather more
>
> philosophical than R-syntax.
>
>
>
>
>
> Here is the data setup:
>
>
>
>    a.frame
> b.frame
>
>    -----------
> ------------
>
>    1. Represents  data from clinical trial A                            1.
> Represents  data from clinical trial B
>
>   2. Two arms, 'ACTIVE' and 'PLACEBO'                              2. Two
> arms, 'ACTIVE' and 'PLACEBO'
>
>    3. The active drug is the same as with Study B              3. The active
> drug is the same as with Study A
>
>    4. The trial design is very similar to Study B                    4. The
> trial design is very similar to Study A
>
>    5. One measurement is a clinical continuous                 5. Does NOT
> have the clinical continuous measure
>
>         measure obtained via laboratory assay                           that
> is available in Study A
>
>    6. Number of randomized subjects = 500                       6. Number of
> randomized subjects = 5,000
>
>    7. A subset of the baseline covariates (call it                 7. A
> subset of the baseline covariates (call it
>
>         a.subset.frame) has 100% commonality
> b.subset.frame) has 100% commonality
>
>         with b.subset.frame
> with a.subset.frame
>
>
> 8. Primary endpoint is time-to-event
>
>
>
>
>
> Here is the analysis setup:
>
>
>
> I have separately split a.frame and b.frame into 'ACTIVE' and 'PLACEBO'
> subjects.
>
>
>
> For the 'PLACEBO' subjects I have entered the a.subset.frame = 
> b.subset.frame baseline
>
> covariates into lrm( ).  The outcome variable is a factor variable 
> representing Study A = 'Y',
>
> so the estimated propensity scores are the estimated probabilities 
> that a 'PLACEBO' subject is
>
> from Study A.  I then, finally, used the %GREEDY algorithm (posted on 
> Mayo Clinic website)
>
> in SAS to match 1-to-many where the Study A subjects are thought of as 
> 'case' subjects and
>
> the Study B subjects are thought of as 'control' subjects. [I know the 
> matching can be done
>
> in R, I'm working on that now.]  The average number of Study B 
> subjects matched to a
>
> single Study A subject is approximately 5.
>
>
>
> I have done a similar analysis for the 'ACTIVE' subjects.
>
>
>
>
>
>
>
> Here is my question:
>
>
>
> At the end, I will combine the Study B matched 'PLACEBO' and 'ACTIVE'
> subjects and
>
> perform a Cox PH regression to compare 'PLACEBO' and 'ACTIVE' - there 
> will be no Study A
>
> subjects in this analysis.  I want to incorporate the clinical 
> continuous measurement "borrowed"
>
> from Study A as a covariate.  When doing this, how should I best take 
> into account the
>
> 1-to-many matching?  Do I need to weight the Study B subjects, or can 
> I simply enter the
>
> matched Study B subjects into a Cox PH regression and ignore the 
> 1-to-many issue?
>
>
>
>
>
> Kind Regards,
>
>
>
>      David
>
>
>
>
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