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Causal Estimands: A Common Language for Missing Data

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Biopharmaceutical Applied Statistics Symposium

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

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Abstract

What are estimands, and what is there connecting with missing data? The topic of estimands is all about interpreting the results of our statistical analyses in the context of the original scientific questions that we intend to answer in a clinical trial. Missing data, a ubiquitous problem in clinical trials, require additional assumptions and choices on how to collect and analyze data that can have an unexpected impact on the interpretation of these analyses.

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Notes

  1. 1.

    We ignore pathological cases such as multiple maxima to keep the discussion simple.

  2. 2.

    Nuisance parameters such as the variance are ignored for now to simplify the exposition.

  3. 3.

    The model can be extended further by allowing R to take on more than two values indicating multiple response patterns. This will be necessary for applying PMMs.

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Author information

Authors and Affiliations

  1. Early Clinical Development, Pfizer Inc., Cambridge, MA, USA

    Steven A. Gilbert & Ye Tan

Authors
  1. Steven A. Gilbert
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  2. Ye Tan
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Corresponding author

Correspondence to Steven A. Gilbert .

Editor information

Editors and Affiliations

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

    Karl E. Peace

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

    Ding-Geng Chen

  3. Boston University, Cambridge, MA, USA

    Sandeep Menon

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Gilbert, S.A., Tan, Y. (2018). Causal Estimands: A Common Language for Missing Data. In: Peace, K., Chen, DG., Menon, S. (eds) Biopharmaceutical Applied Statistics Symposium . ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-7820-0_15

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