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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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 > > > > > ______________________________________________ > R-help at r-project.org 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.
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 > > > > > ______________________________________________ > R-help at r-project.org 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.-------------- next part -------------- A non-text attachment was scrubbed... Name: PGP.sig Type: application/pgp-signature Size: 842 bytes Desc: not available URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20170316/29cecce8/attachment.bin>