We are interested in using substantive model compatible fully conditional specification (smcfcs) for multiple imputation of missing data. I believe this approach will lead to more unbiased estimates than for example mice, as our analysis will consist of Cox proportional hazards regressions including interaction terms.

In smcfcs the substantive model has to be compatible with the imputation model (the imputation model needs to be nested in the substantive model). As we are also interested in non-linear relationships of continuous variables and want to includes splines, we are wondering how complex we can make the substantive model (how many variables can we include).

The substantive model will be a Cox proportional hazards regression and there are several rules of thumb in survival analysis for the complexity of a model: e.g. 10 (or maybe even less) events per variable.

We were wondering whether:

• Do these same rules of thumb apply for the substantive model in smcfcs?
• Do you base the amount of events on the complete cases, or the complete data?
• We have quite a large fraction of missing data (35% of cases), would you want te be more conservative in such cases (i.e. more events per variable)?

It would be great if you could point me to any literature that is available on this topic.

Thank you for your time!

We are interested in using a substantive model compatible fully conditional specification (smcfcs) for multiple imputations of missing data. I believe this approach will lead to more unbiased estimates than for example mice, as our analysis will consist of Cox proportional hazards regressions including interaction terms.

In smcfcs the substantive model has to be compatible with the imputation model (the imputation model needs to be nested in the substantive model). As we are also interested in non-linear relationships of continuous variables and want to includes splines, we are wondering how complex we can make the substantive model (how many variables can we include).

The substantive model will be a Cox proportional hazards regression and there are several rules of thumb in survival analysis for the complexity of a model: e.g. 10 (or maybe even fewer) events per variable.

We were wondering whether:

• Do these same rules of thumb apply to the substantive model in smcfcs?
• Do you base the number of events on the complete cases or the complete data?
• We have quite a large fraction of missing data (35% of cases), would you want to be more conservative in such cases (i.e. more events per variable)?

It would be great if you could point me to any literature that is available on this topic.