On the use of flexible excess hazard regression models for describing long-term breast cancer survival: a case-study using population-based cancer registry data

R. SchaffarEmail author View ORCID ID profile,
A. Belot,
B. Rachet and
L. Woods

BMC Cancer201919:107

https://doi.org/10.1186/s12885-019-5304-2

Received: 29 May 2018
Accepted: 14 January 2019
Published: 28 January 2019

Open Peer Review reports

Abstract

Background

Breast cancer prognosis has dramatically improved over 40 years. There is, however, no proof of population ‘cure’. This research aimed to examine the pattern of long-term excess mortality due to breast cancer and evaluate its determinants in the context of cancer registry data.

Methods

We used data from the Geneva Cancer Registry to identify women younger than 75 years diagnosed with invasive, localised and operated breast cancer between 1995 and 2002. Flexible modelling of excess mortality hazard, including time-dependent (TD) regression parameters, was used to estimate mortality related to breast cancer. We derived a single “final” model using a backward selection procedure and evaluated its stability through sensitivity analyses using a bootstrap technique.

Results

We analysed data from 1574 breast cancer women including 351 deaths (22.3%). The model building strategy retained age at diagnosis (TD), tumour size and grade (TD), chemotherapy and hormonal treatment (TD) as prognostic factors, while the sensitivity analysis on bootstrap samples identified nodes involvement and hormone receptors (TD) as additional long-term prognostic factors but did not identify chemotherapy and hormonal treatment as important prognostic factors.

Conclusions

Two main issues were observed when describing the determinants of long-term survival. First, the modelling strategy presented a lack of robustness, probably due to the limited number of events observed in our study. The second was the misspecification of the model, probably due to confounding by indication. Our results highlight the need for more detailed data and the use of causal inference methods.