Dear Statalist-users,

Hopefully, there is someone who can help me with the following:
I'm trying to perform a survival analysis with inverse probability weighting after multiple imputation.
I've tried to apply the approach as described previously: https://www.statalist.org/forums/for...opensity-score
But after (successful) multiple data imputation (m=10), I don't know how to proceed from here:

Code:
 * Running my propensity score model for M1-M10 and save complete-data estimation results to miest.ster using mi estimate's saving() option.
 mi estimate, saving(miest, replace): logit thergr leeft i.FIGO_2009 i.ni_pb i.ni_loc newnode_dm tumsize i.morf_cat i.invasiedieptegr i.diffgrad newbmi i.cci i.lvsi  

* Obtain multiple-imputation linear predictions and store them as variable xb mi in the original data (m=0).
mi predict xb_mi using miest  

* Apply the inverse-logit transformation to obtain the probabilities.  
quietly mi xeq: generate phat = invlogit(xb_mi)
mi xeq: generate phat = invlogit(xb_mi)  

* Generate IPTW's  
mi xeq: gen psweight=.
mi xeq: replace psweight = (1/phat) if thergr==1
mi xeq: replace psweight = (1/(1-phat)) if thergr==0  

* Set survival time
mi stset vitfup_years [pweight=psweight], failure(vit_stat) id(rn)
If I understand correctly, the propensity scores (phat), and thereby the IPTW (psweight), are already pooled estimates of M1-M10.
They are stored in the original data (m=0) and empty/missing for m=1-10 in the browser.
Should I, therefore, proceed with:

Code:
 stcox i.thergr phat
(And is it necessary to add the propensity score (phat) in the cox-regression while the weights (psweight) are already added during the declaration of survival data?)

Because if I proceed with mi estimate, I end up with only 59 observations which were the complete cases in M0.

Code:
 mi estimate , eform: stcox i.thergr phat
Lastly, this method does not seem to align with the MIte approach to me, or is it?
(Leyrat, C, et al. (available online; in press), "Propensity score analysis with partially observed covariates: How should multiple imputation be used?" Statistical Methods in Medical Research)

Thanks in advance for helping me figure this out!

Kind regards, Ester