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  • Bias-reduced estimators of conditional odds ratios in matched case-control studies with unmatched confounding [Elektronski vir]
    Blagus, Rok
    We study bias-reduced estimators of exponentially transformed parameters in general linear models (GLMs) and show how they can be used to obtain bias-reduced conditional (or unconditional) odds ... ratios in matched case-control studies. Two options are considered and compared: the explicit approach and the implicit approach. The implicit approach is based on the modified score function where bias-reduced estimates are obtained by using iterative procedures to solve the modified score equations. The explicit approach is shown to be a one-step approximation of this iterative procedure. To apply these approaches for the conditional analysis of matched case-control studies, with potentially unmatched confounding and with several exposures, we utilize the relation between the conditional likelihood and the likelihood of the unconditional logit binomial GLM for matched pairs and Cox partial likelihood for matched sets with appropriately setup data. The properties of the estimators are evaluated by using a large Monte Carlo simulation study and an illustration of a real dataset is shown. Researchers reporting the results on the exponentiated scale should use bias-reduced estimators since otherwise the effects can be under or overestimated, where the magnitude of the bias is especially large in studies with smaller sample sizes.
    Source: Biometrical journal [Elektronski vir]. - ISSN 1521-4036 (Vol. 65, iss. 4, [article no.] 2200133, 2023, str. 1-17)
    Type of material - e-article ; adult, serious
    Publish date - 2023
    Language - english
    COBISS.SI-ID - 141664515

    Link(s):

    https://onlinelibrary.wiley.com/doi/full/10.1002/bimj.202200133
    Repository of the University of Ljubljana – RUL
    DOI

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