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ORIGINAL ARTICLES
Year : 2021  |  Volume : 17  |  Issue : 4  |  Page : 982-987

Comparison of hazard models with and without consideration of competing risks to assess the effect of neoadjuvant chemotherapy on locoregional recurrence among breast cancer patients


1 Division of Biostatistics, Kalinga Institute of Medical Sciences, Bhubaneswar, Odisha; Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India
2 Department of Surgical Oncology, BRA Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, India
3 Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India
4 Division of Biostatistics, Kalinga Institute of Medical Sciences, Bhubaneswar, Odisha, India

Correspondence Address:
Sada Nand Dwivedi
Department of Biostatistics, All India Institute of Medical Sciences, Ansari Nagar, New Delhi - 110 029
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jcrt.JCRT_49_19

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Context: While analyzing locoregional recurrences (LRRs), it is necessary to consider distant metastasis as a competing event. Because, later one is more fatal than LRR. It may change ongoing treatment of breast cancer and may alter the chance of LRR. Although some earlier studies assessed the effect of neoadjuvant chemotherapy (NACT) on LRR, they did not use competing risk regression model for it. Aims: To identify the risk factors and predict LRR using competing risk hazard model and to compare them with those using conventional hazard model. Settings and Design: This was a retrospective study from a tertiary care cancer hospital in India. Subjects and Methods: Data of 2114 breast cancer patients undergoing surgery were used from patient's record files (1993–2014). Statistical Analysis: Fine and Gray competing risk regression was used to model time from surgery to LRR, considering distant metastasis and death as the competing events. Further, cause-specific Cox regression was used to model time from surgery to LRR without considering competing risk. Results: Greater than ten positive nodes (hazard ratio [HR] [95% confidence interval (CI)]: 2.19 [1.18–4.03]), skin involvement (HR [95% CI]: 2.75 [1.50–5.05]), NACT (HR [95% CI]: 1.90 [1.06–3.40]), invasive tumor in inner quadrant (HR [95% CI]: 1.78 [0.98–3.24]), and postoperative radiotherapy (HR [95% CI]: 0.52 [0.29–0.94]) were found to be significantly associated with LRR. However, conventional survival analysis ignoring competing risk overestimated cumulative incidence function and underestimated survival. Competing risk regression provided relatively more precise CI. Conclusions: Competing risks, if any, need to be incorporated in the survival analysis. NACT was found to be associated with higher risk for LRR, which may be because of administering it mainly to patients with bad prognosis. Conclusions: Competing risks, if any, need to be incorporated in the survival analysis. NACT was found to be associated with higher risk for LRR, which may be because of administering it mainly to patients with bad prognosis.


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