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A Principal Stratification Approach to Assess the Differences in Prognosis between Cancers Caused by Hormone Replacement Therapy and by Other Factors

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  • Sjolander Arvid

    (Karolinska Institute)

  • Vansteelandt Stijn

    (Universiteit Gent)

  • Humphreys Keith

    (Karolinska Institute)

Abstract

Several recent studies have reported that women who have used hormone replacement therapy (HRT), and developed breast cancer, tend to have a better prognosis than women with breast cancer who have not used HRT. One possible explanation is that tumors caused by HRT are more benign than tumors caused by other factors. Although it is relevant to quantify differences in prognostic factors across subtypes of breast cancer, it is not obvious how to do this correctly. This is because the tumors which occur among women who are treated with HRT are a mixture of HRT-induced and other tumors. We propose a framework based on principal stratification to distinguish women with HRT-induced tumors from women with tumors caused by other factors. To estimate the difference in prognosis for these two groups, we propose two estimation methods, which can be used under both cohort and case-control sampling schemes.

Suggested Citation

  • Sjolander Arvid & Vansteelandt Stijn & Humphreys Keith, 2010. "A Principal Stratification Approach to Assess the Differences in Prognosis between Cancers Caused by Hormone Replacement Therapy and by Other Factors," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-37, June.
    • Handle: RePEc:bpj:ijbist:v:6:y:2010:i:1:n:20
    DOI: 10.2202/1557-4679.1225
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    References listed on IDEAS

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    1. Yannis Jemiai & Andrea Rotnitzky & Bryan E. Shepherd & Peter B. Gilbert, 2007. "Semiparametric estimation of treatment effects given base‐line covariates on an outcome measured after a post‐randomization event occurs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 879-901, November.
    2. Bryan E. Shepherd & Peter B. Gilbert & Yannis Jemiai & Andrea Rotnitzky, 2006. "Sensitivity Analyses Comparing Outcomes Only Existing in a Subset Selected Post-Randomization, Conditional on Covariates, with Application to HIV Vaccine Trials," Biometrics, The International Biometric Society, vol. 62(2), pages 332-342, June.
    3. Arvid Sjölander & Keith Humphreys & Stijn Vansteelandt & Rino Bellocco & Juni Palmgren, 2009. "Sensitivity Analysis for Principal Stratum Direct Effects, with an Application to a Study of Physical Activity and Coronary Heart Disease," Biometrics, The International Biometric Society, vol. 65(2), pages 514-520, June.
    4. Shepherd, Bryan E. & Gilbert, Peter B. & Lumley, Thomas, 2007. "Sensitivity Analyses Comparing Time-to-Event Outcomes Existing Only in a Subset Selected Postrandomization," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 573-582, June.
    5. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    6. van der Laan Mark J., 2008. "Estimation Based on Case-Control Designs with Known Prevalence Probability," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-57, September.
    7. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    8. Peter B. Gilbert & Ronald J. Bosch & Michael G. Hudgens, 2003. "Sensitivity Analysis for the Assessment of Causal Vaccine Effects on Viral Load in HIV Vaccine Trials," Biometrics, The International Biometric Society, vol. 59(3), pages 531-541, September.
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    Cited by:

    1. Pearl Judea, 2011. "Principal Stratification -- a Goal or a Tool?," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-13, March.

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