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Survivor treatment bias, treatment selection bias, and propensity scores in observational research

  • Peter C. Austin
    Correspondence
    Corresponding author. Institute for Clinical Evaluative Sciences, G1 06, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada. Tel.: +1 416-480-6131; fax: +1 416-480-6048.
    Affiliations
    Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada

    Dalla Lana School of Public Health, University of Toronto, Ontario, Canada

    Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
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  • Robert W. Platt
    Affiliations
    Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada

    Department of Pediatrics, McGill University, Montreal, Quebec, Canada
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      We would like to thank the editors for the invitation to comment on the article by Tleyjeh et al. published in this issue of the journal [

      Tleyjeh IM, Ghomrawi HMK, Steckelberg JM, Montori VM, Hoskin TL, Enders F, et al. Conclusion about the association between valve surgery and mortality in an infective endocarditis cohort changed after adjusting for survivor bias. J Clin Epidemiol 2010;63:130–5 [in this issue].

      ]. Tleyjeh et al. address the important issue of survivor treatment bias in observational studies and then propose two statistical methods for accounting for this bias. Studies with time-to-event outcomes in which the exposure of interest occurs during the same period during which outcomes occur can be susceptible to survivor treatment bias, also referred to as “immortal time bias” [
      • Suissa S.
      Immortal time bias in pharmacoepidemiology.
      ] or “time-dependent bias” [
      • van Walraven C.
      • Davis D.
      • Forster A.J.
      • Wells G.A.
      Time-dependent bias was common in survival analyses published in leading clinical journals.
      ,
      • Beyersmann J.
      • Wolkewitz M.
      • Schumacher M.
      The impact of time-dependent bias in proportional hazards modelling.
      ,
      • Beyersmann J.
      • Gastmeier P.
      • Wolkewitz M.
      • Schumacher M.
      An easy mathematical proof showed that time-dependent bias inevitably leads to biased effect estimation.
      ].
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      References

      1. Tleyjeh IM, Ghomrawi HMK, Steckelberg JM, Montori VM, Hoskin TL, Enders F, et al. Conclusion about the association between valve surgery and mortality in an infective endocarditis cohort changed after adjusting for survivor bias. J Clin Epidemiol 2010;63:130–5 [in this issue].

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