Re: [R] propensity scores & imputation
Hi Mr. Gunter, Will do. Thanks, I've not visited stats.stackexchange before. Kind Regards, David -Original Message- From: Bert Gunter [mailto:bgunter.4...@gmail.com] Sent: Thursday, March 16, 2017 7:51 PM To: david.p...@statmetrics.biz Cc: R-help 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 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 A1. > 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 B4. 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@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. PGP.sig Description: PGP signature __ R-help@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.
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 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 A1. > 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 B4. 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@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. __ R-help@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.
[R] propensity scores & imputation
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 A1. 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 B4. 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 PGP.sig Description: PGP signature __ R-help@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.
