[R] propensity scores & imputation
david.paul at statmetrics.biz
Thu Mar 16 18:42:06 CET 2017
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:
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
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'
For the 'PLACEBO' subjects I have entered the a.subset.frame =
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
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'
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
from Study A as a covariate. When doing this, how should I best take into
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
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