Hi Kamarul, I personally prefer the model that adjusts for all of the covariates individually unless you have a very small sample size or a rare outcome. Basically that is a much more flexible model than is assuming that the outcome is a linear function of the propensity score, and so I prefer it unless degrees of freedom are severely limited. Thanks, Liz

This paper talks about how after matching there should be less sensitivity to model specification so in fact it shouldn’t matter too much which model you choose. This is probably the closest literature I know of on this particular topic. http://gking.harvard.edu/files/matchp.pdf Liz On Nov 17, 2015, at 8:34 PM, K Imran M &lt;drki.musa(a)gmail.com&gt; wrote: Hi Liz, Cool. And I guess adjusting the covariates individually also gives a bigger and better picture of the model. By the way, have you come across any literature comparing the two? Many thanks Liz Regards, Kamarul On Tue, Nov 17, 2015 at 11:53 PM, Elizabeth Stuart &lt;estuart(a)jhu.edu&gt; wrote: Hi Kamarul, I personally prefer the model that adjusts for all of the covariates individually unless you have a very small sample size or a rare outcome. Basically that is a much more flexible model than is assuming that the outcome is a linear function of the propensity score, and so I prefer it unless degrees of freedom are severely limited. Thanks, Liz On Nov 16, 2015, at 10:07 AM, K Imran M &lt;drki.musa(a)gmail.com&gt; wrote: Hi, This is my first posting and I apologise if similar questions have been asked before. I have run these R codes (codes mostly taken from Matt Bogart blog). My question would be: 1) (see the lowermost of the codes) Which model specification is the 'best' the describe the adjusted/matched predictive effect of 'treat' upon 're78'? 2) What is the difference between the linear combination of 'treat + pscore' against 'treat + age+educ+black+hispan+married+nodegree+re74+re75' when both are using the matched data generated by 'match.data' ################################### library(MatchIt) data1<-lalonde #matching m.out1<-matchit(treat~age+educ+black+hispan+nodegree+married+ re74+re75,data=data1,method = 'nearest', distance = 'logit') # create matched data based on matchit, giving the output ps as pscore m.data1<-match.data(m.out1,distance='pscore') # generate propensity score manually m.data2<-lalonde head(m.data2) ps.mod<-glm(treat~age+educ+black+hispan+married+ nodegree+re74+re75,data=m.data2,family = binomial(link='logit')) summary(ps.mod) #predict for prosp score head(m.data2) m.data2$psore<-fitted(ps.mod) head(m.data2) dim(m.data2) #do models mod.reg<-lm(re78~treat+age+educ+black+hispan+married+nodegree+re74+re75,data = lalonde) mod.ps1<-lm(re78~treat+age+educ+black+hispan+married+nodegree+re74+re75,data = m.data1) mod.ps11<-lm(re78~treat+pscore,data = m.data1) mod.ps2<-lm(re78~treat+age+educ+black+hispan+married+nodegree+re74+re75,data = m.data2) #restrict to ps between 0.1 to 0.9 m.data3<-m.data2[m.data2$psore>=0.1 & m.data2$psore<=.9,] mod.ps3<-lm(re78~treat+age+educ+black+hispan+married+nodegree+re74+re75,data = m.data3) summary(mod.reg);summary(mod.ps1);summary(mod.ps11);summary(mod.ps2);summary(mod.ps3) ##################### Thank you Kamarul -- Dr. Kamarul Imran Musa, M.D. M.Community.Med. Associate Professor (Epidemiology and Biostatistics) & Public Health Physician Dept of Community Medicine School of Medical Sciences 16150 Universiti Sains Malaysia, Kbg Kerian Kelantan Thomson Reuters researchID: http://www.researcherid.com/rid/G-4864-2010 Google-scholar: http://scholar.google.co.uk/citations?user=aZyayMgAAAAJ&hl=en&authu… blog: http://designdataanalysis.wordpress.com email : drkamarul(a)usm.my , k.musa(a)lancaster.ac.uk - --- MatchIt mailing list served by HUIT List Address: matchit(a)lists.gking.harvard.edu Subscribe/Unsubscribe: http://lists.gking.harvard.edu/mailman/listinfo/ei MatchIt Software and Documentation: http://gking.harvard.edu/matchit/ Browse/Search List Archive: http://lists.gking.harvard.edu/mailman/private/matchit/ Matchit mailing list Matchit(a)lists.gking.harvard.edu To unsubscribe from this list or get other information: https://lists.gking.harvard.edu/mailman/listinfo/matchit -- Dr. Kamarul Imran Musa, M.D. M.Community.Med. Associate Professor (Epidemiology and Biostatistics) & Public Health Physician Dept of Community Medicine School of Medical Sciences 16150 Universiti Sains Malaysia, Kbg Kerian Kelantan Thomson Reuters researchID: http://www.researcherid.com/rid/G-4864-2010 Google-scholar: http://scholar.google.co.uk/citations?user=aZyayMgAAAAJ&hl=en&authu… blog: http://designdataanalysis.wordpress.com email : drkamarul(a)usm.my , k.musa(a)lancaster.ac.uk - --- MatchIt mailing list served by HUIT List Address: matchit(a)lists.gking.harvard.edu Subscribe/Unsubscribe: http://lists.gking.harvard.edu/mailman/listinfo/ei MatchIt Software and Documentation: http://gking.harvard.edu/matchit/ Browse/Search List Archive: http://lists.gking.harvard.edu/mailman/private/matchit/ Matchit mailing list Matchit(a)lists.gking.harvard.edu To unsubscribe from this list or get other information: https://lists.gking.harvard.edu/mailman/listinfo/matchit