The "10% change" idea was never a good one and has not been backed up by simulations. It is quite arbitrary and results in optimistic standard errors of remaining variables. In fact a paper presented at the Joint Statistical Meetings about 3 years ago (I'm sorry I've forgotten the names of the authors) showed that conflicting results are obtained according to whether you apply the 10% to the coefficients or to the odds ratios, and there is no theory to guide the choice. Why risk residual confounding? Form a good model apriori and adjust for all potential confounders; don't base the choice on P-values. Use propensity scores if overfitting is an issue. Frank
farahnazlakhani wrote: > > I am working on my thesis in which i have couple of independent variables > that are categorical in nature and the depndent variable is dichotomus. > Initially I run univariate analysis and added the variables with > significant p-values (p<0.25) in my full model. > I have three confusions. Firstly, I am looking for confounding variables > by using formula "(crude beta-cofficient - adjusted beta-cofficient)/ > crude beta-cofficient x 100" as per rule if the percentage of any variable > is >10% than I have considered that as confounder. I wanted to know that > from initial model i have deducted one variable with insignificant p-value > to form adjusted model. Now how will i know if the variable that i > deducted from initial model was confounder or not? > Secondly, I wanted to know if the percentage comes in negative like > (-17.84%) than will it be considered as confounder or not? I also wanted > to know that confounders should be removed from model? or should be kept > in model? > Lastly, I wanted to know that I am running likelihood ratio test to > identify if the value is falling in critical region or not. So if the > value doesnot fall in critical region than what does it show? what should > I do in this case? In my final reduced model all p-values are significant > but still the value identified via likelihood ratio test is not falling in > critical region. So what does that show? > ----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/Logistic-Regression-tp3578962p3579392.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.
