just a hint for further bootstrapping examples (worked out with R):
"Bootstrap Methods and Their Applications" by A.C. Davison and D.V. Hinkley
cheers
christoph
luciana wrote:
Dear Sirs,
I am a R beginning user: by mean of R I would like to apply the bootstrap to my data in order to test cost differences between independent or paired samples of people affected by a certain disease.
My problem is that even if I am reading the book by Efron (introduction to the bootstrap), looking at the examples in internet and available in R, learning a lot of theoretical things on bootstrap, I can't apply bootstrap with R to my data because of many doubts and difficulties. This is the reason why I have decided to ask the expert for help.
I have a sample of diabetic people, matched (by age and sex) with a control sample. The variable I would like to compare is their drug and hospital monthly cost. The variable cost has a very far from gaussian distribution, but I need any way to compare the mean between the two group. So, in the specific case of a paired sample t-test, I aim at testing if the difference of cost is close to 0. What is the better way to follow for that?
Another question is that sometimes I have missing data in my dataset (for example I have the cost for a patients but not for a control). If I introduce NA or a dot, R doesn't estimate the statistic I need (for instance the mean). To overcome this problem I have replaced the missing data with the mean computed with the remaining part of data. Anyway, I think R can actually compute the mean even with the presence of missing data. Is it right? What can I do?
Thank you very much for your attention and, I hope, your help.
Best wishes
Luciana Scalone
Center of Pharmacoeconomics
University of Milan
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