natalie.vanzuydam
2012-Jul-17 09:10 UTC
[R] Stats question: Comparison of the same individuals during two exposure times
Hi, I'm hoping that someone will be able to help. I would like to compare how covariates associate with the risk of a binary outcome during two periods. Period 1 will be non-exposure to a treatment and period 2 will be exposure to a treatment. The same individuals will be examined in each group but I want to be able to compare the association of certain covariates between the two groups to see if there is a treatment interaction. I've looked at case-crossover designs and time series analysis and don't think that they are suitable. The cohort has longitudinal data so individuals will go onto treatment at different times and the effect of the treatment needs to be administered for a while before it has an effect. The reason why I cannot just go ahead with an exposed vs unexposed design is that most individuals in the cohort end up on the treatment eventually and the unexposed group is very small and lacks power for a meaningful comparison. Is there anyway to compare the same individuals during different exposure times and to look at the effect of different covariates under the exposed and unexposed conditions? Thanks for you help, Natalie ----- Natalie Van Zuydam PhD Student University of Dundee nvanzuydam at dundee.ac.uk -- View this message in context: http://r.789695.n4.nabble.com/Stats-question-Comparison-of-the-same-individuals-during-two-exposure-times-tp4636732.html Sent from the R help mailing list archive at Nabble.com.
Bert Gunter
2012-Jul-17 16:24 UTC
[R] Stats question: Comparison of the same individuals during two exposure times
I would STRONGLY recommend that you talk to your local statistician. Further: 1. R-help is not a statistical consulting forum 2. Remote statistical consulting is very risky due to the inherent difficulty in communicating all essential context of the problem and data. You have been warned! -- Bert On Tue, Jul 17, 2012 at 2:10 AM, natalie.vanzuydam <nvanzuydam@gmail.com>wrote:> Hi, > > I'm hoping that someone will be able to help. I would like to compare how > covariates associate with the risk of a binary outcome during two periods. > Period 1 will be non-exposure to a treatment and period 2 will be exposure > to a treatment. The same individuals will be examined in each group but I > want to be able to compare the association of certain covariates between > the > two groups to see if there is a treatment interaction. I've looked at > case-crossover designs and time series analysis and don't think that they > are suitable. The cohort has longitudinal data so individuals will go onto > treatment at different times and the effect of the treatment needs to be > administered for a while before it has an effect. The reason why I cannot > just go ahead with an exposed vs unexposed design is that most individuals > in the cohort end up on the treatment eventually and the unexposed group is > very small and lacks power for a meaningful comparison. > > Is there anyway to compare the same individuals during different exposure > times and to look at the effect of different covariates under the exposed > and unexposed conditions? > > Thanks for you help, > Natalie > > ----- > Natalie Van Zuydam > > PhD Student > University of Dundee > nvanzuydam@dundee.ac.uk > -- > View this message in context: > http://r.789695.n4.nabble.com/Stats-question-Comparison-of-the-same-individuals-during-two-exposure-times-tp4636732.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org mailing list > 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. >-- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm [[alternative HTML version deleted]]