Inference of Tamoxifen's Effects on Prevention of Breast Cancer from a Randomized Controlled Trial
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The largest randomized, double-blind, placebo-controlled chemoprevention trial, the National Surgical Adjuvant Breast and Bowel Project's Breast Cancer Prevention Trial (NSABP-BCPT), evaluated the efficacy of tamoxifen in the prevention of breast cancer among women at high risk of developing the disease. The trial has reported a reduction of breast cancer incidence for the tamoxifen group. However, the effect of tamoxifen on the time to diagnosis of the disease over the six-year follow-up of the trial has not been fully explored in literature. We propose a flexible semiparametric model to assess the effects of tamoxifen on the incidence of breast cancer as well as time to the diagnosis of the disease, separately, in the framework of a cure-rate model. We used an estimating equation approach to estimating the unknown parameters, and assessed the semiparametric model assumption with a test based on the area between two survival curves. On the NSABP-BCPT data, we found that the treatment of tamoxifen has a substantial effect in the reduction of invasive breast cancer events in estrogen receptor (ER)-positive tumors, but has no effect on ER-negative tumors. Among women who were diagnosed to have ER-positive breast cancer during the study follow-up, there was little difference in terms of time to diagnosis between the two arms. However, tamoxifen may advance the time to breast cancer diagnosis for ER-negative breast cancer, while the incidence of ER-negative tumors is similar in the two treatment arms.
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PMID: 29546253 PMC: 5849265. DOI: 10.15406/bbij.2014.01.00006.
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Likelihood approaches for the invariant density ratio model with biased-sampling data.
Shen Y, Ning J, Qin J Biometrika. 2013; 99(2):363-378.
PMID: 23843663 PMC: 3635710. DOI: 10.1093/biomet/ass008.
Shen Y, Costantino J, Qin J J Natl Cancer Inst. 2008; 100(20):1448-53.
PMID: 18840821 PMC: 2720727. DOI: 10.1093/jnci/djn320.