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AbstractIn assessing the efficacy of a time-varying treatment structural nested models (SNMs) are useful in dealing with confounding by variables affected by earlier treatments. These models often consider treatment allocation and repeated measures at the individual level. We extend SNMMs to clustered observations with time-varying confounding and treatments. We demonstrate how to formulate models with both cluster- and unit-level treatments and show how to derive semiparametric estimators ofdoi:10.1515/ijb-2014-0055 pmid:26115504 fatcat:4bt32yug7rbjhb6vidnklxbm7q