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Causal Inference from Longitudinal Studies with Baseline Randomization

Author

Listed:
  • Toh Sengwee

    (Department of Epidemiology, Harvard School of Public Health)

  • Hernán Miguel A.

    (Department of Epidemiology, Harvard School of Public Health, and Harvard-MIT Division of Health Sciences and Technology)

Abstract

We describe analytic approaches for study designs that, like large simple trials, can be better characterized as longitudinal studies with baseline randomization than as either a pure randomized experiment or a purely observational study. We (i) discuss the intention-to-treat effect as an effect measure for randomized studies, (ii) provide a formal definition of causal effect for longitudinal studies, (iii) describe several methods -- based on inverse probability weighting and g-estimation -- to estimate such effect, (iv) present an application of these methods to a naturalistic trial of antipsychotics on symptom severity of schizophrenia, and (v) discuss the relative advantages and disadvantages of each method.

Suggested Citation

  • Toh Sengwee & Hernán Miguel A., 2008. "Causal Inference from Longitudinal Studies with Baseline Randomization," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-32, October.
    • Handle: RePEc:bpj:ijbist:v:4:y:2008:i:1:n:22
    DOI: 10.2202/1557-4679.1117
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    Cited by:

    1. Miguel Ángel Martínez-González & Nerea Martín-Calvo & Telmo Bretos-Azcona & Silvia Carlos & Miguel Delgado-Rodríguez, 2022. "Mediterranean Diet and Cardiovascular Prevention: Why Analytical Observational Designs Do Support Causality and Not Only Associations," IJERPH, MDPI, vol. 19(20), pages 1-18, October.
    2. Muhammad Badar Habib & Selina Khoo & Tony Morris, 2022. "Motives and Passion of Adults from Pakistan toward Physical Activity," IJERPH, MDPI, vol. 19(6), pages 1-17, March.

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