PURPOSE: Sequential drug treatments in metastatic breast cancer (MBC) are disparate. Clinical trial data includes limited reporting of treatment context, primarily including the number of prior therapies. This study evaluates the relationship between prior treatment time, prior lines of treatment, and survival using a novel visualization technique coupled with statistical analyses. PATIENTS AND METHODS: This retrospective cohort study used a nationwide, de-identified electronic health record-derived database to identify women with hormone receptor-positive, human epidermal growth factor receptor 2-negative MBC diagnosed in 2014 who subsequently received paclitaxel. Images were created, with individual patients represented on the y-axis and time, on the x-axis. Specific treatments were represented by colored bars, with Kaplan-Meier curves overlaying the image. Separate images assessed progression-free survival and overall survival (OS). Hazard ratios (HRs) and 95% CIs from Cox proportional hazards models evaluated the association between prior treatment time and OS. RESULTS: Of 234 patients, median survival from first paclitaxel administration was 20 months (interquartile range, 8-53 months). An inverse relationship was observed between OS after paclitaxel and timing of administration. In adjusted models, each year on treatment prior to paclitaxel was associated with a 16% increased hazard of death after paclitaxel (HR, 1.16; 95% CI, 1.05 to 1.29). CONCLUSION: OS after a specific treatment is dependent on when a drug is given in the disease context, highlighting the potential for an overall OS benefit to be observed on the basis of treatment timing. Prior time on treatment should be considered as a stratifying factor in randomized trials and a confounding factor when examining survival in observational data.
CITATION STYLE
Rocque, G. B., Gilbert, A., Williams, C. P., Kenzik, K. M., Nakhmani, A., Kandhare, P. G., … Azuero, A. (2020). Prior Treatment Time Affects Survival Outcomes in Metastatic Breast Cancer. JCO Clinical Cancer Informatics, (4), 500–513. https://doi.org/10.1200/cci.20.00008
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