Structural Nested Mean Models to Estimate the Effects of Time-Varying Treatments on Clustered Outcomes

He Jiwei (), Stephens-Shields Alisa and Joffe Marshall
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He Jiwei: Department of Biostatistics, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA
Stephens-Shields Alisa: Department of Biostatistics, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA
Joffe Marshall: Department of Biostatistics, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA

The International Journal of Biostatistics, 2015, vol. 11, issue 2, 203-222

Abstract: In 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 of parameters in such models. For unit-level treatments, we consider interference, namely the effect of treatment on outcomes in other units of the same cluster. The properties of estimators are evaluated through simulations and compared with the conventional GEE regression method for clustered outcomes. To illustrate our method, we use data from the treatment arm of a glaucoma clinical trial to compare the effectiveness of two commonly used ocular hypertension medications.

Keywords: clustered observations; time-varying confounding; structural nested mean models (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1515/ijb-2014-0055

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