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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