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Accuracy Loss Due to Selection Bias in Cohort Studies with Left Truncation

Authors

  • Enrique F. Schisterman,

    Corresponding author
    • Division of Epidemiology, Statistics, and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD
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  • Stephen R. Cole,

    1. Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC
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  • Aijun Ye,

    1. Division of Epidemiology, Statistics, and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD
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  • Robert W. Platt

    1. Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, QC, Canada
    2. Department of Pediatrics, McGill University, Montréal, QC, Canada
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Correspondence:

Enrique F. Schisterman, Division of Epidemiology, Statistics, and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, 6100 Executive Boulevard., 7B03, Rockville, MD 20854, USA.

E-mail: schistee@mail.nih.gov

Abstract

Background

Selection is a common problem in paediatric and perinatal epidemiology, and truncation can be thought of as missing person time that can result in selection bias. Left truncation, also known as late or staggered entry, may induce selection bias and/or adversely affect precision. There are two kinds of left truncation: fixed left truncation where the start of follow-up is initiated at a set time, and variable left truncation where follow-up begins at a stochastically varying time-point.

Methods

Using data from a time-to-pregnancy study, augmented by a simulation study, we demonstrate the effects of fixed and variable truncation on estimates of the hazard ratio.

Results

First, fixed or variable non-differential left truncation results in a loss of precision. Fixed or variable differential left truncation results in a bias either towards or away from the null as well as a loss of precision. The extent and direction of this bias is a function of the size and direction of the association between exposure and outcome, and occurs in common scenarios and under a wide range of conditions.

Conclusions

As demonstrated in simulation studies, selection bias due to left truncation could have a serious impact on inferences, especially in the case of fixed or variable differential left truncation. When present in epidemiologic studies, proper accounting for left truncation is just as important as proper accounting for right censoring.

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