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Related Experiment Videos

Differences in risk estimations from a hospital and a population-based case-control study.

D W West, K L Schuman, J L Lyon

    International Journal of Epidemiology
    |June 1, 1984
    PubMed
    Summary

    Respondent selection impacts cervical cancer risk estimates. Hospital-based studies may yield lower risk due to case-like controls, potentially overmatching on related factors like social class.

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    Area of Science:

    • Epidemiology
    • Oncology
    • Biostatistics

    Background:

    • Cervical cancer (in situ and invasive) presents a significant public health concern.
    • Case-control studies are crucial for identifying disease risk factors.
    • The method of selecting participants can influence study outcomes.

    Purpose of the Study:

    • To investigate how different respondent selection methods affect risk estimates in cervical cancer studies.
    • To compare hospital-based and population-based case-control study designs.
    • To evaluate the impact of control group selection on risk factor associations.

    Main Methods:

    • Conducted a hospital-based and population-based case-control study in urban Utah.
    • Identified cervical cancer cases (N=409 population, N=124 hospital) via the Utah Cancer Registry.

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  • Recruited population-based controls (N=379) via random-digit dialing and hospital-based controls (N=150) from gynecology clinics, frequency-matched by age.

    • Main Results:

    • Hospital-based studies yielded lower risk estimates, likely due to more case-like control attributes.
    • Stratified analysis revealed lower adjusted risk estimates for social class in population-based studies.
    • Close social class matching in hospital-based studies led to overmatching on related risk factors.

    Conclusions:

    • Respondent selection significantly impacts risk estimates in cervical cancer case-control studies.
    • Hospital-based control selection can introduce bias through overmatching on factors correlated with social class.
    • Careful consideration of control selection methods is essential for robust case-control study design.