Empirical likelihood-based estimation of the treatment effect in a pretest-posttest study

Chiung Yu Huang, Jing Qin, Dean A. Follmann

Research output: Contribution to journalArticlepeer-review

Abstract

The pretest-posttest study design is commonly used in medical and social science research to assess the effect of a treatment or an intervention. Recently, interest has been rising in developing inference procedures that improve efficiency while relaxing assumptions used in the pretest-posttest data analysis, especially when the posttest measurement might be missing. In this article we propose a semiparametric estimation procedure based on empirical likelihood (EL) that incorporates the common baseline covariate information to improve efficiency. The proposed method also yields an asymptotically unbiased estimate of the response distribution. Thus functions of the response distribution, such as the median, can be estimated straight-forwardly, and the EL method can provide a more appealing estimate of the treatment effect for skewed data. We show that, compared with existing methods, the proposed EL estimator has appealing theoretical properties, especially when the working model for the underlying relationship between the pretest and posttest measurements is misspecified. A series of simulation studies demonstrates that the EL-based estimator outperforms its competitors when the working model is misspecified and the data are missing at random. We illustrate the methods by analyzing data from an AIDS clinical trial (ACTG 175).

Original languageEnglish (US)
Pages (from-to)1270-1280
Number of pages11
JournalJournal of the American Statistical Association
Volume103
Issue number483
DOIs
StatePublished - Sep 2008

Keywords

  • Auxiliary information
  • Biased sampling
  • Causal inference
  • Observational study
  • Survey sampling

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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