【正文】 aggregate data as subgroups Trials included from IPD: 7 Patients included: 1333 Trials included from aggregate data: 4 Patients included: 1009 Pooling of main (treatment) effect estimate arm using Fixedeffects trial reference | number | Effect [95% Conf. Interval] % Weight + IPD | LCSG 773 | CAMS | ... | ... Subgroup effect | + Aggregate | belgium | EORTC 08861 | ... | ... Subgroup effect | Tests of effect size = 1: IPD z = p = Aggregate z = p = Inclusion of aggregate data: Screen output Inclusion of aggregate data: Forest plot I P DL C S G 7 7 3C A M SM R C L U 1 1S L O V E N I AG E TC B 0 4 C B 8 6I TA L YK O R E AS u b t o t a l ( I sq u a r e d = 0 . 0 % , p = 0 . 7 4 0 )A g g r e g a t eb e l g i u mE O R TC 0 8 8 6 1L I L L EG E TC B 0 5 C B 8 6S u b t o t a l ( I sq u a r e d = 0 . 0 % , p = 0 . 9 6 4 )r e f e r e n ce n u m b e rt r i a l1 . 1 2 ( 0 . 8 3 , 1 . 5 3 )1 . 0 3 ( 0 . 7 7 , 1 . 3 8 )0 . 9 6 ( 0 . 7 4 , 1 . 2 4 )0 . 8 9 ( 0 . 5 4 , 1 . 4 9 )1 . 1 4 ( 0 . 8 0 , 1 . 6 2 )0 . 6 9 ( 0 . 4 0 , 1 . 2 0 )1 . 1 6 ( 0 . 7 6 , 1 . 7 6 )1 . 0 2 ( 0 . 9 0 , 1 . 1 6 )1 . 4 6 ( 1 . 0 7 , 1 . 9 8 )1 . 6 4 ( 0 . 9 1 , 2 . 9 6 )1 . 5 7 ( 1 . 0 6 , 2 . 3 2 )1 . 4 4 ( 1 . 1 3 , 1 . 8 3 )1 . 4 8 ( 1 . 2 6 , 1 . 7 4 )E f f e ct ( 9 5 % C I )1 8 . 1 81 9 . 9 22 6 . 1 26 . 4 81 3 . 8 55 . 6 99 . 7 61 0 0 . 0 02 8 . 6 17 . 7 91 7 . 5 64 6 . 0 31 0 0 . 0 0W e i g h t%1 4. 2 5Advanced syntax example: non “eclass” estimation mand ipdmetan (u[1,1]/V[1,1]) (1/sqrt(V[1,1])) , study(trialid) eform ad(, byad) lcols(evrate=_d % Event rate) rcols(u[1,1] % oE(o) V[1,1] % V(o)) forest(nooverall nostats nowt) : sts test arm if subgroup==0, mat(u V) Effect estimate amp。 parison with metan ? Covariate interactions ? Combining AD with IPD ? Advanced syntax ? The forestplot mand ? Interface with ipdmetan ? Standalone use and “stacking” ? Summary and Conclusion Introduction to IPD metaanalysis ? Metaanalysis (MA): ? Use statistical methods to bine results of “similar” trials to give a single estimate of effect ? Increase power amp。 = ratio of tau178。 spacing。 Laird, ???? = 1 ???? ??(??) 2 + ??2 ? Straightforward, but currently messy in Stata where ???? ?? = 1 ?????? Treatmentcovariate interactions ? Assessment of patientlevel covariate interactions is a great advantage of IPD ? Arguably best done with “onestage” ? Main effects amp。 | I178。 CIs。 correls estimated simultaneously Flexible amp。 extendable model structure Natural extension of AD MA Easily presentable in forest plots Applicable to any set of effect estimates and SEs (incl. interactions) Negligible difference to 1S in most mon scenarios Cons Requires more statistical expertise Challenging in certain situations, . randomeffects with timetoevent data Not a natural fit with forest plots Only a single estimate can be pooled, which limits plexity (. interactions) Theoretically inferior in (at least) some scenarios Example data ?