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The traditional approach to variable selection is to find variables that contribute the most to predicting a new response. Recently I learned of an alternative to this. In modeling variables that determine the effect of a treatment--as for example in a clinical trial of a pharmaceutical--the variable is said to be qualitatively interacting with treatment if, leaving other things fixed, a change in that variable can create a change in which treatment is most effective. These variables are not always strong predictors of the effect but may be important for a physician when deciding on treatment for individual patients. In her PhD thesis Lacey Gunter developed a method for selecting these qualitatively interacting variables that could be missed by algorithms that base selection on prediction. Recently I have worked with her on extending these methods to other models including logistic regression and Cox proportional hazard regression models.

I have two questions:

  1. What do you think about the value of these new methods?
  2. In the case of the traditional methods what approach do you prefer? Criteria such as AIC, BIC, Mallows Cp, F tests for entering or dropping variables in stepwise, forward and backward...

The first paper on this came out in Gunter, L., Zhu, J and Murphy, S. A. (2009). Variable selection for qualitative interactions. Statistical Methodology doi:10, 1016/j.stamet.2009.05.003.

The next paper appeared in Gunter,L., Zhu, J. and Murphy, S. A. (2011). Variable selection of qualitative interactions in personalized medicine while controlling the familywise error rate. Journal of Biopharmaceutical Statistics 21, 1063-1078.

The next one appeared in a special issue on variable selection Gunter, L., Chernick, M. R. and Sun, J. (2011). A simple method for variable selection in regression with respect to treatment selection. Pakistan Journal of Statistics and Operations Research 7: 363-380.

You can find the papers at the journal websites. You may have to purchase the article. I might have the pdf files for these articles. Can articles be attached to these posts? Lacey and I have just completed a monograph on this topic which will be published as a SpringerBrief later this year. To answer William Huber's question the first paper on this came out in Statistical Methodology Gunter, L., Zhu, J and Murphy, S. A. (2009). Variable selection for qualitative interactions. doi:10, 1016/j.stamet.2009.05.003. The next paper appeared in the Journal of Biopharmaceutical Statistics Gunter,L., Zhu, J. and Murphy, S. A. (2011). variable selection of qualitative interactions in personalized medicine while controlling the familywise error rate. 21, 1063-1078. the next one appeared in the Pakistan Journal of Statistics and Operations Research in a special issue on variable selection Gunter, L., Chernick, M. R. and Sun, J. (2011). A simple method for variable selection in regression with respect to treatment selection. Pakistan Journal of Statistics and Operations Research 7: 363-380. You can find the papers at the journal websites. You may have to purchase the article. I might have the pdf files for these articles. Can articles be attached to these posts? Lacey and I have just completed a monograph on this topic which will be pulbished as a SpringerBrief later this year.

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Maybe I'm not following - if there is an a priori reason to suspect effect modification, then how do these new methods differ from, for example, including interaction terms in the list of "candidate" variables for model selection? – Macro May 3 '12 at 17:13
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(1) One or more lines seem to have been lost in this question. I guess it might continue "stepwise, forward and backward, ..." (2) Model identification and variable selection have been extensively discussed here. E.g., searching on +model +variable +selection presents 145 threads at this point. Narrowing that search will likely answer the second question. (3) To facilitate answers to the first question, could you provide a link or explicit references to this research? – whuber May 3 '12 at 17:31
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This is a matter of including a variable that interacts with the treatment. But it is a qualitative interaction not just a simple interaction. To interact the two lines must not be parallel. To qualitatively interact they must cross in the interval in which the variable is defined. So the idea is to find a variable that qualitatively interacts. This is different from picking variables and interaction terms that improve the fit or prediction. – Michael Chernick May 3 '12 at 19:25
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Thanks for taking the opportunity to respond, Michael. Perhaps a key point to bring up is that this site is not a discussion site, but rather a Q&A site. With that comes some slightly different modalities of communication. The FAQ covers this in some detail. Occasionally the threading can get a bit lost, but it's actually surprisingly rare I find, once one gets a little more experience with the general scheme of things. Cheers. – cardinal May 3 '12 at 20:40
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Michael, yes, the SE system takes some getting used to and is not perfect. But it does make sense and it is consistent. One thing we aim for is ongoing improvement: unlike list servers and bulletin boards, questions (and answers) can be modified; this is expected. Ultimately, we would like a thread to start with a single, well stated, complete question that stands on its own without reference to the comment thread; then it should continue with one or more well-written, well-attributed canonical answers. With this ideal in mind, @cardinal's suggestions may make more sense to you. – whuber May 4 '12 at 14:21
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