issue with survfitJM

[huyendn]

I got this error when trying to fit the dynamic prediction Error in contrasts<-(*tmp*, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels I know this error occurs when the categorical only has 1 level. However, if we do prediction for one patient, a categorical could have only 1 level for that patient. In your example code (https://erandrinopoulou.github.io/EducationalCorner_JMpred/) , the data for the patient for prediction has some categorical with one level. Could you please help look into this?

[drizopoulos]

Check JMbayes2

—— Professor of Biostatistics Erasmus Medical Center Rotterdam The Netherlands

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Από: huyendn @.> Στάλθηκε: Thursday, August 10, 2023 2:57:10 PM Προς: drizopoulos/JMbayes @.> Κοιν.: Subscribed @.***> Θέμα: [drizopoulos/JMbayes] issue with survfitJM (Issue #104)

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I got this error when trying to fit the dynamic prediction Error in contrasts<-(tmp, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels I know this error occurs when the categorical only has 1 level. However, if we do prediction for one patient, a categorical could have only 1 level for that patient. In your example code (https://erandrinopoulou.github.io/EducationalCorner_JMpred/) , the data for the patient for prediction has some categorical with one level. Could you please help look into this?

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[huyendn]

Thanks for the fast response. I just tried predict function in JMbayes2 with jm object and still got the same message error.

predSurv <- predict(jointm1.bayes, newdata = newdata_5, process = "event",

• return_newdata = TRUE) Error in contrasts<-(*tmp*, value = contr.funs[1 + isOF[nn]]) : contrasts can be applied only to factors with 2 or more levels

[huyendn]

Hello,

Do you know what might possibly cause this issue?

Thank you

[zoezhang106]

Hi,

I encountered the same problem recently and solved by converting categorical variables to factors. You may also want to check if each of the factors is with 2 or more levels.

Hope this helps.

[ellenerickson]

Make sure in "newdata_5" you are defining your factor variables to have two levels even if you're predicting with only one level. For example, if you have 2 time points and your categorical variable is gender, define it as:

newdata_5$gender <- factor(c('female','female'), levels = c('female','male'))