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Collaborative Targeted Maximum Likelihood Estimation to Assess Causal Effects in Observational Studies

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Biopharmaceutical Applied Statistics Symposium

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

Abstract

Observational studies can address a wide variety of issues in medicine and public health. Targeted learning (TL) provides a framework for unbiased estimation of treatment effects using these data. TL relies on two core methodologies, targeted minimum loss-based estimation (TMLE), an efficient double robust estimator, and data adaptive super learning. Collaborative TMLE (C-TMLE) is an extension of TMLE that is particularly effective when there is a sparsity of information in the data. C-TMLE provides an automated approach to constructing parsimonious models for treatment and censoring mechanisms. These models are built in response to residual bias not addressed in an initial outcome regression. C-TMLE can stabilize estimates and reduce mean squared error in high dimensional and other sparse data settings.

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Author information

Authors and Affiliations

  1. Putnam Data Sciences, LLC, 85 Putnam Avenue, Cambridge, MA, 02139, USA

    Susan Gruber

  2. School of Public Health, University of California at Berkeley, 108 Haviland Hall, Berkeley, CA, 94720-7360, USA

    Mark van der Laan

Authors
  1. Susan Gruber
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  2. Mark van der Laan
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Corresponding author

Correspondence to Susan Gruber .

Editor information

Editors and Affiliations

  1. Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, USA

    Karl E. Peace

  2. School of Social Work & Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA

    Ding-Geng Chen

  3. Boston University, Cambridge, Massachusetts, USA

    Sandeep Menon

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Gruber, S., van der Laan, M. (2018). Collaborative Targeted Maximum Likelihood Estimation to Assess Causal Effects in Observational Studies. In: Peace, K., Chen, DG., Menon, S. (eds) Biopharmaceutical Applied Statistics Symposium . ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7826-2_1

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