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

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  • He Jiwei
  • Stephens-Shields Alisa
  • Joffe Marshall

    (Department of Biostatistics, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA)

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.

Suggested Citation

  • He Jiwei & Stephens-Shields Alisa & Joffe Marshall, 2015. "Structural Nested Mean Models to Estimate the Effects of Time-Varying Treatments on Clustered Outcomes," The International Journal of Biostatistics, De Gruyter, vol. 11(2), pages 203-222, November.
    • Handle: RePEc:bpj:ijbist:v:11:y:2015:i:2:p:203-222:n:4
    DOI: 10.1515/ijb-2014-0055
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    References listed on IDEAS

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    1. Sobel, Michael E., 2006. "What Do Randomized Studies of Housing Mobility Demonstrate?: Causal Inference in the Face of Interference," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1398-1407, December.
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