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. 2018 Dec;172(3):659-669.
doi: 10.1007/s10549-018-4935-3. Epub 2018 Aug 30.

Time to death in breast cancer patients as an indicator of treatment response

Steven A Narod  1   2 , Vasily Giannakeas  3 , Victoria Sopik  3
Affiliations

Time to death in breast cancer patients as an indicator of treatment response

Steven A Narod et al. Breast Cancer Res Treat. 2018 Dec.

Abstract

Purpose: To describe the mortality experience of women who die of breast cancer in the 20-year period post-diagnosis using various metrics, including annual mortality rates, Kaplan-Meier survival curves and time-to-death histograms.

Methods: We generated three visual representations of SEER-based and hospital-based breast cancer patient cohorts using three different metrics of mortality.

Results: The greatest impact of most prognostic factors was on the probability of latent metastases present after treatment, but for some factors the primary impact was on the time to death for those women with metastases.

Conclusions: The use of time-to-death statistics to display mortality benefits for treated versus untreated women helps facilitate the distinction between treatments which increase the likelihood of cure and treatments that delay cancer growth.

Keywords: Breast cancer; Mortality; Progression; Survival.

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Conflict of interest statement

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Figures

Fig. 1
Fig. 1
a Impact of ER status on annual mortality rates. b Impact of ER status on actuarial survival. c Impact of ER status on time to death
Fig. 2
Fig. 2
a Impact of nodal status on annual mortality rates. b Impact of nodal status on actuarial survival. c. Impact of nodal status on time to death
Fig. 3
Fig. 3
a Impact of tumour size on annual mortality rates. b Impact of tumour size on actuarial survival. c Impact of tumour size on time to death
Fig. 4
Fig. 4
a Impact of tumour grade on annual mortality rates. b Impact of tumour grade on actuarial survival. c Impact of tumour grade on time to death
Fig. 5
Fig. 5
a Impact of age at diagnosis on annual mortality rates. b Impact of age at diagnosis on actuarial survival. c Impact of age of diagnosis on time to death
Fig. 6
Fig. 6
a Impact of race on annual mortality rates. b Impact of race on actuarial survival. c Impact of race on time to death
Fig. 7
Fig. 7
Survival from ER-negative cancer, observed versus exponential (“expected”) decline
Fig. 8
Fig. 8
The relationship between annual mortality and the percent of deaths which occur in years 0–5
Fig. 9
Fig. 9
a Impact of 30% reduction in deaths on annual mortality rates, ER-positive patients in SEER. b Impact of 30% reduction in deaths on actuarial survival, ER-positive patients in SEER. c Impact of 30% reduction in deaths on time to death, ER-positive patients in SEER
Fig. 10
Fig. 10
a Impact of doubling time to death on annual hazard rates, ER-positive patients in SEER. b Impact of doubling time to death on actuarial survival, ER-positive patients in SEER. c Impact of doubling time to death on time to death, ER-positive patients in SEER
Fig. 11
Fig. 11
a Impact of tamoxifen therapy on annual mortality, ER-positive patients in Banting database. b Impact of tamoxifen therapy on actuarial survival, ER-positive patients in Banting database. c Impact of tamoxifen therapy on time to death, ER-positive patients in Banting database
See this image and copyright information in PMC

References

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