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Selection Bias in Population-Based Cancer Case–Control Studies Due to Incomplete Sampling Frame Coverage
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Details:
  • Pubmed ID:
    22490319
  • Pubmed Central ID:
    PMC3645306
  • Funding:
    CA47147/CA/NCI NIH HHS/United States
    CA67264/CA/NCI NIH HHS/United States
    CD000712/CD/ODCDC CDC HHS/United States
    K05 CA152715/CA/NCI NIH HHS/United States
    R01 CA047147/CA/NCI NIH HHS/United States
    R01 CA067264/CA/NCI NIH HHS/United States
    UL1 RR025011/RR/NCRR NIH HHS/United States
    UL1 TR000427/TR/NCATS NIH HHS/United States
  • Document Type:
  • Collection(s):
  • Description:
    Background

    Increasing numbers of individuals are choosing to opt out of population-based sampling frames due to privacy concerns. This is especially a problem in the selection of controls for case–control studies, as the cases often arise from relatively complete population-based registries, whereas control selection requires a sampling frame. If opt out is also related to risk factors, bias can arise.

    Methods

    We linked breast cancer cases who reported having a valid driver’s license from the 2004–2008 Wisconsin women’s health study (N = 2,988) with a master list of licensed drivers from the Wisconsin Department of Transportation (WDOT). This master list excludes Wisconsin drivers that requested their information not be sold by the state. Multivariate-adjusted selection probability ratios (SPR) were calculated to estimate potential bias when using this driver’s license sampling frame to select controls.

    Results

    A total of 962 cases (32%) had opted out of the WDOT sampling frame. Cases age <40 (SPR = 0.90), income either unreported (SPR = 0.89) or greater than $50,000 (SPR = 0.94), lower parity (SPR = 0.96 per one-child decrease), and hormone use (SPR = 0.93) were significantly less likely to be covered by the WDOT sampling frame (α = 0.05 level).

    Conclusions

    Our results indicate the potential for selection bias due to differential opt out between various demographic and behavioral subgroups of controls. As selection bias may differ by exposure and study base, the assessment of potential bias needs to be ongoing.

    Impact

    SPRs can be used to predict the direction of bias when cases and controls stem from different sampling frames in population-based case–control studies.