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From |
Richard Williams <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Transformed values in logistic regression |

Date |
Thu, 26 Aug 2004 22:59:47 -0500 |

At 08:27 PM 8/26/2004 -0700, Ricardo Ovaldia wrote:

While I am not an expert in creatinine (whatever that is) I am inclined to agree with you. You can always plug in implausible/impossible numbers and come up with a prediction, e.g. how much would somebody make if they had -2,000,000 years of education? I've never heard of a rule which says that x = 1 has to be a plausible or even possible value. For presentation purposes, you might want to scale your variables in ways which make them easier to understand and present (e.g. measure income in thousands of dollars rather than in dollars) but it is not essential. There may be other good reasons for not doing what you are doing, but the reason given seems odd to me, unless maybe it violates some sort of convention in your field. If you want to make this reviewer happy, maybe you could measure creatinine in milligrams instead of grams or whatever happens to be reasonable so that a 1 unit increase in x is possible.Specifically we were interested in modeling case-control status as a function of several patient covariates including serum creatinine which in our data ranges from 0.11 to 1.98. Because of skewness and to make the odds ratio independent of the units measurement, we decided to log-transform the creatinine values before entering them into our logistic model. However the reviewer wrote "Using a log-transform for creatine is absurd because a 1-unit increase in ln(x) is equivalent to increasing x by a factor of 2.718 which is in the realm of impossibility" Is he correct? Isn't the coefficient estimated such that the predicted values are within the range of the data and this only a problem if you attempt to extrapolate beyond the data range? What I am missing?

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Richard Williams, Notre Dame Dept of Sociology

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**References**:**st: time series data***From:*Christopher F Baum <[email protected]>

**st: Transformed values in logistic regression***From:*Ricardo Ovaldia <[email protected]>

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