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I fit my data with linear mix model using y and log-transformed y like below:

fit1 = lmer(y~Visit+Treatment+(1|Subject.ID))
fit2 = lmer(log(y)~Visit+Treatment+(1|Subject.ID))

Treatment effect in fit1 is not significant, but it is significant in fit2 (with log(y) ).

I'm confused about how should I interpret the model result.

Can anyone explain what is the correct way to interpret mixed model result when dependent variable is log-transformed vs not log-transformed.

Thanks a lot in advance!

zesla
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    This has come up before. One example [here](https://stats.stackexchange.com/a/93103/7071) and another [here](https://stats.stackexchange.com/a/44156/7071). – dimitriy Jan 15 '19 at 23:02
  • [Here](https://stats.stackexchange.com/questions/362556/linear-mixed-effect-model-interpretation-with-log-transformed-dependent-variable) is another possible duplicate. Also, it would be a good idea to check that the residuals are normally distributed. – Robert Long Jan 16 '19 at 11:22

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