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Steve,

While this wouldn't be an experiment (the patient factors in the charts are an observation you make, not something you design) this is a perfectly reasonable analysis. Just like folks run regression models on cross-sectional data all the time that isn't experimentally designed, so too you can run logit models on data from a chart audit. In fact, my first experience with what's not called an unconditional logit model was with just such data - case file data from a state prison system that I used to predict some particular inmate choices.

While this wouldn't be an experiment (the patient factors in the charts are an observation you make, not something you design) this is a perfectly reasonable analysis. Just like folks run regression models on cross-sectional data all the time that isn't experimentally designed, so too you can run logit models on data from a chart audit. In fact, my first experience with what's not called an unconditional logit model was with just such data - case file data from a state prison system that I used to predict some particular inmate choices.

Keith, very helpful, thank you! Since we can't test the design described above, do you have any guidance for sample size that you think would generate at least somewhat reliable utilities? These are physicians so not a dime a dozen unfortunately. Is 200 OK? How much leeway downward could this go and still generate something useful? Trying to get some sense of sensitivity to how the parameters could change and still avoid things breaking down. Thanks!

It's hard to say without knowing how many levels you'll build for each attribute. A rule of thumb for logit models would be something like this, adopted from Peduzzi et al (1996): your sample size should be greater or equal to 10kp/t where k is the number of parameters in your model, p is the number of drug options a respondent might choose and t is the number of observations (charts) you pull per respondent.

k is a tricky one. In this kind of model, an unconditional logit model, the number of parameters is (total # of levels - # of attributes)*(p-1).

Our new book (http://www.sawtoothsoftware.com/81-products/conjoint-analysis-software/1852-new-advanced-conjoint-analysis-book) has a chapter on models and experiments like this one and discusses issues like sample size, design and analysis of the unconditional logit model.

k is a tricky one. In this kind of model, an unconditional logit model, the number of parameters is (total # of levels - # of attributes)*(p-1).

Our new book (http://www.sawtoothsoftware.com/81-products/conjoint-analysis-software/1852-new-advanced-conjoint-analysis-book) has a chapter on models and experiments like this one and discusses issues like sample size, design and analysis of the unconditional logit model.

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