Page 27 - Contributed Paper Session (CPS) - Volume 5

CPS657 Folorunso Serifat A. et al.
trial. These models estimate the cured proportion and also the probability of
survival.
Mixture cure model: This one can evaluate the percentage of
patients cured and the survival function of the uncured patients
Boag (1949) & Berkson and Gage (1952)].
Non-Mixture Cure Model: This can also be called the Bounded Cumulative
Hazard Model (BCH), [Yakovlev et al (1993)].
The following highlighted points are the essential importance of statistical
cure.
• Patient survival is one of the most important questions in cancer
research.
• Cure models give more information about patient survival.
• Cure models predict the proportion of patients cured from cancer.
• They predicts time until cured.
• Estimate survival time of patients not cured.
Seppa et.al. (2009) applied a mixture cure fraction model with random effects
to cause-specifc survival data of female breast cancer patients collected by the
population-based Finnish Cancer Registry. Two sets of random effects were
used to capture the regional variation in the cure fraction and in the survival
of the non-cured patients, respectively. The random effects allowed the fitting
of the cure fraction model to the sparse regional data and the estimation of
the regional variation in 10-year cause-specific breast cancer survival with a
parsimonious number of parameters which led to the capital of Finland to
clearly stood out from the rest, but since then all the 21 hospital districts have
achieved approximately the same level of survival.
Verdecchia et.al. (1998) also asserted that traditional parametric survival
models that assume that all patients are susceptible to eventually die from the
disease itself are often inadequate in describing the survival experience of
cancer patients. It is conceivable that many patients are in fact cured in the
sense that their lifetime is not shortened by the cancer but their mortality rates
remain the same as if they had avoided the cancer. At an individual level it is
practically impossible to determine for sure whether a patient is cured or not.
However, for many cancers it appears to be possible in principle to identify the
cure fraction, i.e. the proportion of patients, whose mortality will not be
elevated as compared with the mortality rates in a similar cancer-free
population. Francisci (2008) concluded that Cure fraction models have become
increasingly popular in population based studies on cancer survival performed
for individual countries, but also in international comparisons, and their
usefulness is motivated. The proportion cured and the mean survival time for
the non-cured patients can be useful summary parameters for detailed
assessment of the differences in survival.

16 | I S I W S C 2 0 1 9