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Statistics for weighing benefits and harms in a proposed genetic substudy of a randomized cancer prevention trial

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  • Stuart G. Baker
  • Barnett S. Kramer

Abstract

Summary. When evaluating potential interventions for cancer prevention, it is necessary to compare benefits and harms. With new study designs, new statistical approaches may be needed to facilitate this comparison. A case in point arose in a proposed genetic substudy of a randomized trial of tamoxifen versus placebo in asymptomatic women who were at high risk for breast cancer. Although the randomized trial showed that tamoxifen substantially reduced the risk of breast cancer, the harms from tamoxifen were serious and some were life threaten‐ing. In hopes of finding a subset of women with inherited risk genes who derive greater bene‐fits from tamoxifen, we proposed a nested case–control study to test some trial subjects for various genes and new statistical methods to extrapolate benefits and harms to the general population. An important design question is whether or not the study should target common low penetrance genes. Our calculations show that useful results are only likely with rare high penetrance genes.

Suggested Citation

  • Stuart G. Baker & Barnett S. Kramer, 2005. "Statistics for weighing benefits and harms in a proposed genetic substudy of a randomized cancer prevention trial," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(5), pages 941-954, November.
    • Handle: RePEc:bla:jorssc:v:54:y:2005:i:5:p:941-954
    DOI: 10.1111/j.1467-9876.2005.00522.x
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    Cited by:

    1. Ying Huang & Eric Laber, 2016. "Personalized Evaluation of Biomarker Value: A Cost-Benefit Perspective," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(1), pages 43-65, June.
    2. Holly Janes & Margaret S. Pepe & Ying Huang, 2014. "A Framework for Evaluating Markers Used to Select Patient Treatment," Medical Decision Making, , vol. 34(2), pages 159-167, February.
    3. Janes Holly & Brown Marshall D. & Huang Ying & Pepe Margaret S., 2014. "An Approach to Evaluating and Comparing Biomarkers for Patient Treatment Selection," The International Journal of Biostatistics, De Gruyter, vol. 10(1), pages 1-23, May.
    4. Xiaofei Wang & Haibo Zhou, 2010. "Design and Inference for Cancer Biomarker Study with an Outcome and Auxiliary-Dependent Subsampling," Biometrics, The International Biometric Society, vol. 66(2), pages 502-511, June.

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