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A post-hoc Unweighted Analysis of Counter-Matched Case-Control Data

Author

Listed:
  • Rakovski Cyril

    (Department of Mathematics, Chapman University, Orange, CA 92866, USA)

  • Langholz Bryan

    (Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA)

Abstract

Informative sampling based on counter-matching risk set subjects on exposure correlated with a variable of interest has been shown to be an efficient alternative to simple random sampling; however, the opposite is true when correlation between the two covariates is absent. Thus, the counter-matching design will entail substantial gains in statistical efficiency compared to simple random sampling at a first stage of analyses focused by design on variables correlated with the counter-matching variable but will lose efficiency at a second stage of analyses aimed at variables independent of the counter-matching variable and not conceived as a part of the initial study. In an effort to recover efficiency in such second stage of analyses scenarios, we considered a naive analysis of the effect of a dichotomous covariate on the disease rates in the population that ignores the underlying counter-matching sampling design. We derive analytical expressions for the bias and variance and show that when the counter-matching and the new dichotomous variable of interest are uncorrelated and a multiplicative main effects model holds, such an analysis is advantageous over the standard “weighted” approach, especially when the counter-matching variable is rare and in such scenarios the efficiency gains exceeds 80%. Moreover, we consider all possible conceptual violations of the required assumptions and show that moderate departures from the above-mentioned requirements lead to negligible levels of bias; numerical values for the bias under common scenarios are provided. The method is illustrated via an analysis of BRCA1/2 deleterious mutations in the radiation treatment counter-matched WECARE study of second breast cancer.

Suggested Citation

  • Rakovski Cyril & Langholz Bryan, 2015. "A post-hoc Unweighted Analysis of Counter-Matched Case-Control Data," The International Journal of Biostatistics, De Gruyter, vol. 11(2), pages 223-232, November.
  • Handle: RePEc:bpj:ijbist:v:11:y:2015:i:2:p:223-232:n:1
    DOI: 10.1515/ijb-2014-0018
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    References listed on IDEAS

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    1. Bryan Langholz, 2007. "Use of Cohort Information in the Design and Analysis of Case‐Control Studies," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 120-136, March.
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