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Implementing double-robust estimators of causal effects

Listed author(s):
  • Richard Emsley


    (Biostatistics, Health Methodology Research Group, The University of Manchester)

  • Mark Lunt

    (Arthritis Research Campaign Epidemiology Unit, The University of Manchester)

  • Andrew Pickles

    (Biostatistics, Health Methodology Research Group, The University of Manchester)

  • GraHam Dunn

    (Biostatistics, Health Methodology Research Group, The University of Manchester)

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    This article describes the implementation of a double-robust estimator for pretest-posttest studies (Lunceford and Davidian, 2004, Statistics in Medicine 23: 2937-2960) and presents a new Stata command (dr) that carries out the procedure. A double-robust estimator gives the analyst two opportunities for obtaining unbiased inference when adjusting for selection effects such as confounding by allowing for different forms of model misspecification; a double-robust estimator also can offer increased efficiency when all the models are correctly specified. We demonstrate the results with a Monte Carlo simulation study, and we show how to implement the double-robust estimator on a single simulated dataset, both manually and by using the dr command. Copyright 2008 by StataCorp LP.

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    Article provided by StataCorp LP in its journal Stata Journal.

    Volume (Year): 8 (2008)
    Issue (Month): 3 (September)
    Pages: 334-353

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    Handle: RePEc:tsj:stataj:v:8:y:2008:i:3:p:334-353
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    1. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
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