propensity scores & imputation

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propensity scores & imputation

David Paul-3
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

 


______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

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Re: propensity scores & imputation

Bert Gunter-2
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 <[hidden email]> 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
>
>
>
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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Re: propensity scores & imputation

David Paul-3
Hi Mr. Gunter,

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


Kind Regards,

        David

-----Original Message-----
From: Bert Gunter [mailto:[hidden email]]
Sent: Thursday, March 16, 2017 7:51 PM
To: [hidden email]
Cc: R-help <[hidden email]>
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 <[hidden email]> 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
>
>
>
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.


______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

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