Causal inference from longitudinal studies with baseline randomization

Int J Biostat. 2008 Oct 19;4(1):Article 22. doi: 10.2202/1557-4679.1117.

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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Antipsychotic Agents / therapeutic use
  • Biostatistics / methods*
  • Female
  • Humans
  • Longitudinal Studies / statistics & numerical data*
  • Male
  • Models, Statistical
  • Patient Compliance / statistics & numerical data
  • Probability
  • Random Allocation
  • Randomized Controlled Trials as Topic / statistics & numerical data
  • Schizophrenia / drug therapy
  • Treatment Outcome

Substances

  • Antipsychotic Agents