Causal Inference: Efficacy and Mechanism Evaluation

Sabine Landau () and Richard Emsley ()
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Sabine Landau: King’s College London, Department of Biostatistics and Health Informatics
Richard Emsley: King’s College London, Department of Biostatistics and Health Informatics

Chapter 100 in Principles and Practice of Clinical Trials, 2022, pp 1981-2002 from Springer

Abstract: Abstract In randomized trials, the primary analysis is usually based on an intention-to-treat approach which answers the question “What is the effect of offering treatment?” There are many other questions that investigators could pose such as “Does this treatment work if it is received?” “What factors make the treatment work better?” and “How does the treatment work?” These questions require alternative analysis approaches based on statistical methods drawn from the causal inference literature, including instrumental variables and causal mediation analysis. This chapter will define relevant causal estimands and describe methods that can be used to estimate them, their underlying assumptions, and the estimation procedures. The methods will be illustrated using examples drawn from the literature.

Keywords: Confounding; Controlled direct effect; Efficacy; Estimand; Instrumental variables methods; Mechanism; Mediation analysis; Nonadherence; Process variable; Treatment effect modification (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-52636-2_137

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DOI: 10.1007/978-3-319-52636-2_137

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