[R] propensity scores & imputation

[Bert Gunter]

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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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